DDS Case Study 2 RMarkdown
Sean McWhirter
11/30/2019
#Link to YouTube Presentation: https://youtu.be/GTze_LtqRXQ
#Hello and thank you for providing the opportunity to provide my insight on this challenge that the company is facing. We will walk through the data set that was provided, look at some of the variables, build a classification model tha twill tell us the liklihood of an employee leaving, and finally, we will use linear regression to predict th monthly income of a set of employees.
#Upload data and take first look
#File Upload
work=read.csv("C:/Users/seans/Desktop/DDS/Unit 14 and 15 Case Study 2/CaseStudy2-data.csv", header=TRUE, sep=",", stringsAsFactors = TRUE)
saveRDS(work, file ="work.Rds")
readRDS(work, file ="work.Rds")## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## 7 7 41 No Travel_Rarely 1283 Research & Development
## 8 8 37 No Travel_Rarely 309 Sales
## 9 9 34 No Travel_Rarely 1333 Sales
## 10 10 34 No Travel_Frequently 653 Research & Development
## 11 11 43 No Travel_Rarely 823 Research & Development
## 12 12 28 No Non-Travel 280 Human Resources
## 13 13 35 No Travel_Rarely 950 Research & Development
## 14 14 30 No Travel_Rarely 202 Sales
## 15 15 46 No Travel_Rarely 991 Human Resources
## 16 16 31 No Non-Travel 1188 Sales
## 17 17 32 No Travel_Rarely 498 Research & Development
## 18 18 46 No Non-Travel 1144 Research & Development
## 19 19 34 No Travel_Rarely 181 Research & Development
## 20 20 44 No Travel_Rarely 170 Research & Development
## 21 21 36 No Travel_Rarely 913 Research & Development
## 22 22 48 No Travel_Rarely 817 Sales
## 23 23 43 No Travel_Frequently 1001 Research & Development
## 24 24 31 No Travel_Frequently 715 Sales
## 25 25 33 No Travel_Rarely 1069 Research & Development
## 26 26 44 No Travel_Rarely 1099 Sales
## 27 27 38 No Travel_Rarely 1245 Sales
## 28 28 33 Yes Travel_Rarely 603 Sales
## 29 29 53 Yes Travel_Rarely 1168 Sales
## 30 30 52 No Non-Travel 715 Research & Development
## 31 31 32 No Travel_Frequently 689 Sales
## 32 32 33 No Travel_Rarely 267 Research & Development
## 33 33 46 No Travel_Rarely 488 Sales
## 34 34 35 No Travel_Rarely 982 Research & Development
## 35 35 32 No Travel_Rarely 1373 Research & Development
## 36 36 32 No Travel_Frequently 1311 Research & Development
## 37 37 35 Yes Travel_Rarely 737 Sales
## 38 38 36 Yes Travel_Rarely 1456 Sales
## 39 39 56 No Travel_Rarely 206 Human Resources
## 40 40 31 No Travel_Frequently 793 Sales
## 41 41 58 No Non-Travel 350 Sales
## 42 42 45 No Travel_Rarely 954 Sales
## 43 43 49 No Travel_Rarely 464 Research & Development
## 44 44 42 No Travel_Rarely 1059 Research & Development
## 45 45 23 No Travel_Rarely 373 Research & Development
## 46 46 42 No Travel_Frequently 748 Research & Development
## 47 47 32 Yes Travel_Rarely 964 Sales
## 48 48 38 No Travel_Rarely 770 Sales
## 49 49 18 Yes Travel_Frequently 1306 Sales
## 50 50 27 No Non-Travel 691 Research & Development
## 51 51 38 No Travel_Rarely 362 Research & Development
## 52 52 58 No Travel_Rarely 1055 Research & Development
## 53 53 44 No Travel_Rarely 1117 Research & Development
## 54 54 45 No Non-Travel 805 Research & Development
## 55 55 31 No Travel_Rarely 746 Research & Development
## 56 56 41 No Travel_Rarely 548 Research & Development
## 57 57 42 Yes Travel_Frequently 933 Research & Development
## 58 58 29 No Travel_Rarely 1370 Research & Development
## 59 59 37 No Non-Travel 728 Research & Development
## 60 60 25 Yes Travel_Frequently 599 Sales
## 61 61 36 Yes Travel_Rarely 530 Sales
## 62 62 36 No Travel_Rarely 325 Research & Development
## 63 63 31 No Travel_Rarely 1154 Sales
## 64 64 18 No Non-Travel 1431 Research & Development
## 65 65 24 Yes Travel_Rarely 984 Research & Development
## 66 66 32 No Travel_Rarely 334 Research & Development
## 67 67 35 No Travel_Rarely 1017 Research & Development
## 68 68 23 No Travel_Rarely 571 Research & Development
## 69 69 35 No Travel_Frequently 664 Research & Development
## 70 70 37 No Travel_Rarely 1372 Research & Development
## 71 71 41 No Travel_Rarely 642 Research & Development
## 72 72 28 No Travel_Rarely 866 Sales
## 73 73 35 Yes Travel_Rarely 556 Research & Development
## 74 74 41 No Travel_Rarely 167 Research & Development
## 75 75 52 Yes Travel_Rarely 1030 Sales
## 76 76 30 No Travel_Frequently 721 Research & Development
## 77 77 34 No Travel_Rarely 971 Sales
## 78 78 46 No Travel_Rarely 563 Sales
## 79 79 45 No Non-Travel 1052 Sales
## 80 80 31 No Travel_Rarely 1276 Research & Development
## 81 81 37 Yes Travel_Rarely 1141 Research & Development
## 82 82 35 No Travel_Rarely 1370 Research & Development
## 83 83 38 No Travel_Frequently 508 Research & Development
## 84 84 37 No Travel_Rarely 1319 Research & Development
## 85 85 36 No Travel_Rarely 429 Research & Development
## 86 86 59 No Travel_Rarely 715 Research & Development
## 87 87 47 No Travel_Rarely 703 Sales
## 88 88 35 Yes Travel_Rarely 622 Research & Development
## 89 89 27 No Travel_Frequently 994 Sales
## 90 90 46 No Travel_Rarely 705 Sales
## 91 91 38 No Travel_Frequently 1490 Research & Development
## 92 92 29 Yes Travel_Rarely 408 Sales
## 93 93 37 Yes Travel_Rarely 1373 Research & Development
## 94 94 35 No Travel_Rarely 670 Research & Development
## 95 95 50 No Travel_Rarely 1452 Research & Development
## 96 96 42 No Travel_Rarely 635 Sales
## 97 97 46 No Travel_Rarely 228 Sales
## 98 98 37 No Non-Travel 1040 Research & Development
## 99 99 29 No Travel_Frequently 410 Research & Development
## 100 100 34 No Travel_Rarely 401 Research & Development
## 101 101 27 No Travel_Rarely 1377 Research & Development
## 102 102 31 No Travel_Rarely 326 Sales
## 103 103 34 No Non-Travel 1381 Sales
## 104 104 23 No Travel_Rarely 885 Research & Development
## 105 105 53 No Travel_Rarely 447 Research & Development
## 106 106 26 No Travel_Rarely 1384 Research & Development
## 107 107 29 No Non-Travel 1496 Research & Development
## 108 108 55 No Travel_Rarely 189 Human Resources
## 109 109 46 Yes Travel_Rarely 261 Research & Development
## 110 110 32 No Travel_Rarely 529 Research & Development
## 111 111 28 No Travel_Frequently 773 Research & Development
## 112 112 52 No Travel_Rarely 1053 Research & Development
## 113 113 52 No Travel_Rarely 994 Research & Development
## 114 114 35 No Non-Travel 1225 Research & Development
## 115 115 35 No Travel_Rarely 1361 Sales
## 116 116 37 No Non-Travel 1252 Sales
## 117 117 25 No Travel_Rarely 1356 Sales
## 118 118 29 No Travel_Rarely 1378 Research & Development
## 119 119 41 No Travel_Rarely 465 Research & Development
## 120 120 34 No Travel_Rarely 404 Research & Development
## 121 121 36 No Non-Travel 427 Research & Development
## 122 122 21 No Travel_Rarely 501 Sales
## 123 123 45 No Travel_Rarely 930 Sales
## 124 124 38 No Travel_Rarely 243 Sales
## 125 125 29 No Travel_Frequently 442 Sales
## 126 126 35 No Travel_Rarely 1224 Sales
## 127 127 32 Yes Travel_Frequently 1125 Research & Development
## 128 128 45 No Travel_Rarely 556 Research & Development
## 129 129 43 No Travel_Rarely 930 Research & Development
## 130 130 46 No Travel_Rarely 1319 Sales
## 131 131 29 No Travel_Rarely 991 Sales
## 132 132 49 No Travel_Rarely 1313 Sales
## 133 133 42 No Travel_Frequently 1368 Research & Development
## 134 134 39 No Travel_Rarely 492 Research & Development
## 135 135 43 No Travel_Frequently 559 Research & Development
## 136 136 40 No Travel_Frequently 791 Research & Development
## 137 137 29 Yes Travel_Rarely 805 Research & Development
## 138 138 54 No Travel_Rarely 1441 Research & Development
## 139 139 39 Yes Travel_Rarely 1122 Research & Development
## 140 140 37 No Travel_Rarely 1189 Sales
## 141 141 56 No Travel_Rarely 1369 Research & Development
## 142 142 58 No Travel_Rarely 848 Research & Development
## 143 143 46 No Travel_Rarely 566 Research & Development
## 144 144 37 No Travel_Rarely 228 Sales
## 145 145 40 No Travel_Rarely 1202 Research & Development
## 146 146 29 No Non-Travel 746 Sales
## 147 147 33 No Travel_Rarely 931 Research & Development
## 148 148 33 No Travel_Rarely 1242 Sales
## 149 149 24 No Travel_Rarely 477 Research & Development
## 150 150 28 No Travel_Rarely 580 Research & Development
## 151 151 31 Yes Travel_Rarely 1365 Sales
## 152 152 32 No Travel_Rarely 929 Sales
## 153 153 32 No Travel_Rarely 906 Sales
## 154 154 41 No Travel_Rarely 447 Research & Development
## 155 155 34 No Travel_Rarely 546 Research & Development
## 156 156 30 No Non-Travel 641 Sales
## 157 157 40 No Travel_Rarely 444 Sales
## 158 158 39 Yes Travel_Frequently 203 Research & Development
## 159 159 43 No Travel_Frequently 1082 Research & Development
## 160 160 42 No Travel_Frequently 458 Research & Development
## 161 161 30 No Travel_Rarely 852 Research & Development
## 162 162 28 Yes Non-Travel 1366 Research & Development
## 163 163 33 No Travel_Rarely 1216 Sales
## 164 164 42 No Travel_Rarely 916 Research & Development
## 165 165 35 No Travel_Rarely 1232 Sales
## 166 166 20 Yes Travel_Rarely 500 Sales
## 167 167 49 No Travel_Rarely 301 Research & Development
## 168 168 33 No Travel_Rarely 922 Research & Development
## 169 169 52 No Travel_Rarely 956 Research & Development
## 170 170 29 No Travel_Rarely 1107 Research & Development
## 171 171 30 No Travel_Rarely 501 Sales
## 172 172 47 No Non-Travel 1169 Research & Development
## 173 173 46 No Travel_Frequently 1034 Research & Development
## 174 174 33 No Travel_Rarely 536 Sales
## 175 175 53 No Travel_Rarely 1376 Sales
## 176 176 21 No Travel_Rarely 984 Research & Development
## 177 177 55 Yes Travel_Rarely 725 Research & Development
## 178 178 26 Yes Travel_Frequently 342 Research & Development
## 179 179 32 No Travel_Frequently 967 Sales
## 180 180 38 No Travel_Rarely 702 Sales
## 181 181 35 No Travel_Rarely 819 Research & Development
## 182 182 38 No Travel_Rarely 1009 Sales
## 183 183 43 No Travel_Frequently 957 Research & Development
## 184 184 39 No Travel_Rarely 1089 Research & Development
## 185 185 39 No Travel_Frequently 443 Research & Development
## 186 186 28 No Travel_Rarely 1158 Research & Development
## 187 187 25 No Travel_Frequently 772 Research & Development
## 188 188 47 No Travel_Rarely 1225 Sales
## 189 189 50 No Travel_Rarely 797 Research & Development
## 190 190 28 No Non-Travel 120 Sales
## 191 191 30 No Travel_Rarely 1275 Research & Development
## 192 192 31 No Travel_Frequently 1125 Sales
## 193 193 46 No Travel_Rarely 1485 Research & Development
## 194 194 48 No Travel_Rarely 163 Sales
## 195 195 26 No Travel_Rarely 683 Research & Development
## 196 196 51 No Travel_Rarely 833 Research & Development
## 197 197 51 No Travel_Rarely 432 Research & Development
## 198 198 49 No Travel_Rarely 174 Sales
## 199 199 44 No Travel_Rarely 477 Research & Development
## 200 200 24 No Travel_Rarely 823 Research & Development
## 201 201 40 Yes Non-Travel 1479 Sales
## 202 202 32 No Travel_Rarely 548 Research & Development
## 203 203 32 No Travel_Rarely 859 Research & Development
## 204 204 28 Yes Travel_Rarely 654 Research & Development
## 205 205 32 No Travel_Rarely 1401 Sales
## 206 206 44 No Travel_Rarely 661 Research & Development
## 207 207 43 Yes Travel_Rarely 1372 Sales
## 208 208 38 Yes Travel_Rarely 1180 Research & Development
## 209 209 30 No Travel_Rarely 201 Research & Development
## 210 210 41 No Travel_Rarely 802 Sales
## 211 211 41 No Travel_Rarely 582 Research & Development
## 212 212 42 No Travel_Rarely 265 Sales
## 213 213 37 No Travel_Rarely 1225 Research & Development
## 214 214 42 No Travel_Rarely 932 Research & Development
## 215 215 27 No Travel_Rarely 1115 Research & Development
## 216 216 40 No Travel_Rarely 896 Research & Development
## 217 217 38 No Travel_Rarely 1495 Research & Development
## 218 218 48 No Travel_Frequently 117 Research & Development
## 219 219 30 No Travel_Rarely 438 Research & Development
## 220 220 26 No Travel_Rarely 583 Research & Development
## 221 221 34 No Travel_Rarely 304 Sales
## 222 222 45 Yes Travel_Frequently 306 Sales
## 223 223 33 No Non-Travel 530 Sales
## 224 224 28 No Travel_Rarely 1423 Research & Development
## 225 225 38 No Travel_Rarely 1206 Research & Development
## 226 226 30 No Travel_Rarely 911 Research & Development
## 227 227 35 No Travel_Rarely 464 Research & Development
## 228 228 22 No Travel_Rarely 1136 Research & Development
## 229 229 24 No Travel_Rarely 1353 Sales
## 230 230 49 No Travel_Rarely 465 Research & Development
## 231 231 45 No Travel_Rarely 1329 Research & Development
## 232 232 35 No Travel_Rarely 890 Sales
## 233 233 47 Yes Travel_Frequently 1093 Sales
## 234 234 54 No Travel_Rarely 1147 Sales
## 235 235 43 No Travel_Frequently 394 Sales
## 236 236 37 Yes Travel_Rarely 625 Sales
## 237 237 42 No Travel_Rarely 300 Research & Development
## 238 238 32 No Travel_Rarely 646 Research & Development
## 239 239 25 No Travel_Rarely 309 Human Resources
## 240 240 50 No Travel_Rarely 813 Research & Development
## 241 241 42 No Travel_Rarely 319 Research & Development
## 242 242 45 No Travel_Frequently 364 Research & Development
## 243 243 29 No Travel_Rarely 1328 Research & Development
## 244 244 25 Yes Travel_Rarely 383 Sales
## 245 245 29 No Travel_Rarely 718 Research & Development
## 246 246 29 No Travel_Rarely 1086 Research & Development
## 247 247 31 No Travel_Rarely 1232 Research & Development
## 248 248 32 No Travel_Rarely 234 Sales
## 249 249 41 No Travel_Frequently 1200 Research & Development
## 250 250 41 No Travel_Rarely 1276 Sales
## 251 251 33 No Non-Travel 1313 Research & Development
## 252 252 36 No Non-Travel 1229 Sales
## 253 253 45 No Travel_Rarely 252 Research & Development
## 254 254 31 Yes Travel_Rarely 359 Human Resources
## 255 255 40 No Non-Travel 218 Research & Development
## 256 256 43 No Non-Travel 343 Research & Development
## 257 257 31 No Travel_Frequently 444 Sales
## 258 258 30 No Travel_Rarely 855 Sales
## 259 259 34 No Travel_Rarely 1480 Sales
## 260 260 25 No Travel_Rarely 977 Research & Development
## 261 261 31 Yes Travel_Frequently 703 Sales
## 262 262 29 Yes Travel_Rarely 224 Research & Development
## 263 263 33 Yes Travel_Rarely 813 Research & Development
## 264 264 29 No Travel_Frequently 1404 Sales
## 265 265 24 No Travel_Rarely 1127 Research & Development
## 266 266 35 No Travel_Rarely 809 Research & Development
## 267 267 34 No Travel_Frequently 829 Research & Development
## 268 268 32 Yes Travel_Rarely 1259 Research & Development
## 269 269 33 No Travel_Frequently 1303 Research & Development
## 270 270 30 No Travel_Rarely 1176 Research & Development
## 271 271 45 No Non-Travel 589 Sales
## 272 272 34 No Travel_Rarely 1130 Research & Development
## 273 273 27 No Travel_Rarely 728 Sales
## 274 274 36 No Travel_Rarely 329 Sales
## 275 275 55 No Travel_Rarely 692 Research & Development
## 276 276 56 No Travel_Rarely 718 Research & Development
## 277 277 22 No Travel_Rarely 253 Research & Development
## 278 278 43 No Travel_Frequently 185 Research & Development
## 279 279 25 Yes Travel_Rarely 240 Sales
## 280 280 55 No Travel_Rarely 1229 Research & Development
## 281 281 43 No Travel_Rarely 1179 Sales
## 282 282 41 No Travel_Rarely 263 Research & Development
## 283 283 40 No Travel_Rarely 1194 Research & Development
## 284 284 31 No Travel_Rarely 691 Sales
## 285 285 38 No Travel_Rarely 433 Human Resources
## 286 286 33 No Travel_Rarely 589 Research & Development
## 287 287 22 Yes Travel_Rarely 391 Research & Development
## 288 288 34 No Travel_Rarely 1346 Research & Development
## 289 289 36 No Travel_Frequently 1467 Sales
## 290 290 35 No Travel_Rarely 384 Sales
## 291 291 45 No Travel_Rarely 974 Research & Development
## 292 292 36 No Non-Travel 1105 Research & Development
## 293 293 27 No Travel_Rarely 894 Research & Development
## 294 294 47 No Non-Travel 1162 Research & Development
## 295 295 40 No Travel_Rarely 898 Human Resources
## 296 296 35 No Travel_Rarely 1490 Research & Development
## 297 297 34 No Travel_Frequently 1069 Research & Development
## 298 298 34 Yes Travel_Frequently 658 Research & Development
## 299 299 31 Yes Non-Travel 335 Research & Development
## 300 300 28 Yes Travel_Rarely 1157 Research & Development
## 301 301 32 No Travel_Frequently 379 Sales
## 302 302 35 No Travel_Frequently 853 Sales
## 303 303 55 No Travel_Rarely 1441 Research & Development
## 304 304 35 No Travel_Rarely 1395 Research & Development
## 305 305 58 Yes Travel_Rarely 289 Research & Development
## 306 306 24 No Non-Travel 1269 Research & Development
## 307 307 50 No Travel_Rarely 804 Research & Development
## 308 308 34 No Non-Travel 697 Research & Development
## 309 309 55 No Travel_Frequently 135 Research & Development
## 310 310 25 No Travel_Rarely 810 Sales
## 311 311 34 No Travel_Rarely 1351 Research & Development
## 312 312 40 No Travel_Rarely 302 Research & Development
## 313 313 31 Yes Travel_Frequently 561 Research & Development
## 314 314 51 No Travel_Rarely 1169 Research & Development
## 315 315 60 No Travel_Rarely 1179 Sales
## 316 316 47 No Travel_Rarely 955 Sales
## 317 317 34 No Travel_Frequently 303 Sales
## 318 318 24 No Travel_Rarely 771 Research & Development
## 319 319 39 No Travel_Rarely 1498 Sales
## 320 320 19 No Travel_Rarely 265 Research & Development
## 321 321 32 No Non-Travel 1184 Research & Development
## 322 322 34 No Travel_Rarely 1397 Research & Development
## 323 323 30 No Travel_Frequently 1012 Research & Development
## 324 324 46 No Travel_Rarely 1003 Research & Development
## 325 325 39 No Travel_Rarely 466 Research & Development
## 326 326 27 No Travel_Rarely 618 Research & Development
## 327 327 44 No Travel_Rarely 1313 Research & Development
## 328 328 42 No Travel_Rarely 557 Research & Development
## 329 329 38 No Travel_Frequently 1394 Research & Development
## 330 330 42 No Travel_Rarely 1147 Human Resources
## 331 331 49 No Travel_Rarely 809 Research & Development
## 332 332 32 Yes Travel_Rarely 1045 Sales
## 333 333 44 Yes Travel_Frequently 429 Research & Development
## 334 334 27 No Travel_Rarely 608 Research & Development
## 335 335 59 No Non-Travel 1420 Human Resources
## 336 336 33 Yes Travel_Frequently 827 Research & Development
## 337 337 35 No Travel_Frequently 1182 Sales
## 338 338 34 No Travel_Frequently 618 Research & Development
## 339 339 50 Yes Travel_Frequently 959 Sales
## 340 340 27 No Travel_Rarely 1240 Research & Development
## 341 341 36 No Travel_Rarely 852 Research & Development
## 342 342 28 No Travel_Frequently 193 Research & Development
## 343 343 33 No Travel_Rarely 654 Research & Development
## 344 344 30 No Travel_Frequently 160 Research & Development
## 345 345 30 No Travel_Rarely 1329 Sales
## 346 346 39 No Travel_Rarely 119 Sales
## 347 347 34 No Travel_Frequently 1003 Research & Development
## 348 348 26 No Travel_Frequently 1479 Research & Development
## 349 349 44 No Non-Travel 111 Research & Development
## 350 350 56 No Travel_Rarely 832 Research & Development
## 351 351 42 No Travel_Rarely 1396 Research & Development
## 352 352 31 No Travel_Rarely 218 Sales
## 353 353 40 No Travel_Frequently 902 Research & Development
## 354 354 40 No Non-Travel 1142 Research & Development
## 355 355 41 No Travel_Rarely 796 Sales
## 356 356 34 No Travel_Rarely 1326 Sales
## 357 357 26 No Travel_Rarely 841 Research & Development
## 358 358 29 Yes Travel_Rarely 408 Research & Development
## 359 359 32 No Travel_Rarely 427 Research & Development
## 360 360 36 No Travel_Rarely 1403 Research & Development
## 361 361 52 No Travel_Rarely 699 Research & Development
## 362 362 29 Yes Travel_Frequently 746 Sales
## 363 363 52 Yes Travel_Rarely 266 Sales
## 364 364 40 No Travel_Rarely 1416 Research & Development
## 365 365 55 No Travel_Rarely 282 Research & Development
## 366 366 56 No Travel_Frequently 906 Sales
## 367 367 34 No Travel_Frequently 135 Research & Development
## 368 368 40 No Travel_Rarely 1342 Sales
## 369 369 23 No Travel_Rarely 977 Research & Development
## 370 370 32 No Travel_Rarely 1018 Research & Development
## 371 371 31 No Travel_Frequently 853 Research & Development
## 372 372 30 No Non-Travel 1398 Sales
## 373 373 45 No Travel_Frequently 1249 Research & Development
## 374 374 37 No Travel_Rarely 1470 Research & Development
## 375 375 20 No Travel_Rarely 727 Sales
## 376 376 50 No Travel_Frequently 333 Research & Development
## 377 377 36 Yes Travel_Rarely 885 Research & Development
## 378 378 42 No Travel_Rarely 419 Sales
## 379 379 29 No Travel_Rarely 942 Research & Development
## 380 380 30 Yes Travel_Rarely 138 Research & Development
## 381 381 47 No Travel_Rarely 1482 Research & Development
## 382 382 26 No Travel_Rarely 1355 Human Resources
## 383 383 48 No Travel_Rarely 1221 Sales
## 384 384 23 No Travel_Rarely 541 Sales
## 385 385 51 Yes Travel_Rarely 1323 Research & Development
## 386 386 38 No Travel_Rarely 1333 Research & Development
## 387 387 35 No Travel_Frequently 944 Sales
## 388 388 29 No Travel_Rarely 232 Research & Development
## 389 389 36 No Travel_Rarely 530 Sales
## 390 390 21 Yes Travel_Frequently 756 Sales
## 391 391 34 No Travel_Rarely 1111 Sales
## 392 392 26 No Travel_Rarely 1443 Sales
## 393 393 31 No Non-Travel 976 Research & Development
## 394 394 60 No Travel_Frequently 1499 Sales
## 395 395 42 No Travel_Frequently 1271 Research & Development
## 396 396 31 No Travel_Rarely 192 Research & Development
## 397 397 46 No Travel_Rarely 1009 Research & Development
## 398 398 34 Yes Travel_Rarely 1107 Human Resources
## 399 399 56 No Non-Travel 667 Research & Development
## 400 400 44 No Travel_Frequently 1193 Research & Development
## 401 401 30 No Travel_Rarely 317 Research & Development
## 402 402 42 No Non-Travel 926 Research & Development
## 403 403 47 No Travel_Rarely 207 Research & Development
## 404 404 36 No Travel_Rarely 711 Research & Development
## 405 405 35 No Travel_Rarely 1343 Research & Development
## 406 406 43 No Travel_Rarely 782 Research & Development
## 407 407 31 No Travel_Rarely 196 Sales
## 408 408 36 No Travel_Rarely 917 Research & Development
## 409 409 53 No Travel_Rarely 1219 Sales
## 410 410 43 No Travel_Rarely 589 Research & Development
## 411 411 36 No Travel_Rarely 1278 Human Resources
## 412 412 41 No Travel_Rarely 918 Sales
## 413 413 37 No Travel_Rarely 408 Research & Development
## 414 414 32 No Travel_Frequently 1318 Sales
## 415 415 34 Yes Travel_Frequently 296 Sales
## 416 416 35 No Non-Travel 1180 Research & Development
## 417 417 30 No Non-Travel 1116 Research & Development
## 418 418 36 No Travel_Frequently 566 Research & Development
## 419 419 37 No Travel_Rarely 977 Research & Development
## 420 420 29 No Travel_Rarely 986 Research & Development
## 421 421 34 No Travel_Rarely 1153 Research & Development
## 422 422 37 No Non-Travel 1063 Research & Development
## 423 423 31 Yes Travel_Rarely 249 Sales
## 424 424 18 No Non-Travel 1124 Research & Development
## 425 425 28 No Travel_Rarely 736 Sales
## 426 426 43 No Travel_Rarely 415 Sales
## 427 427 30 No Non-Travel 990 Research & Development
## 428 428 21 No Travel_Rarely 996 Research & Development
## 429 429 32 Yes Travel_Rarely 515 Research & Development
## 430 430 34 No Travel_Rarely 470 Research & Development
## 431 431 27 No Travel_Rarely 1377 Sales
## 432 432 40 No Non-Travel 458 Research & Development
## 433 433 49 Yes Travel_Frequently 1475 Research & Development
## 434 434 29 No Travel_Rarely 590 Research & Development
## 435 435 40 No Travel_Rarely 658 Sales
## 436 436 27 No Travel_Rarely 1130 Sales
## 437 437 28 Yes Travel_Rarely 890 Research & Development
## 438 438 26 No Travel_Rarely 157 Research & Development
## 439 439 50 Yes Travel_Rarely 869 Sales
## 440 440 38 No Travel_Rarely 827 Research & Development
## 441 441 32 No Travel_Frequently 1316 Research & Development
## 442 442 29 No Travel_Rarely 1082 Research & Development
## 443 443 28 Yes Travel_Rarely 529 Research & Development
## 444 444 44 No Travel_Rarely 528 Human Resources
## 445 445 46 No Travel_Rarely 734 Research & Development
## 446 446 47 No Travel_Rarely 465 Research & Development
## 447 447 38 No Travel_Frequently 1189 Research & Development
## 448 448 42 No Travel_Rarely 1128 Research & Development
## 449 449 38 No Travel_Frequently 653 Research & Development
## 450 450 45 No Non-Travel 336 Sales
## 451 451 33 No Travel_Rarely 501 Research & Development
## 452 452 44 No Travel_Rarely 1467 Research & Development
## 453 453 35 Yes Travel_Rarely 303 Sales
## 454 454 33 No Travel_Rarely 217 Sales
## 455 455 48 No Travel_Rarely 1236 Research & Development
## 456 456 40 No Travel_Rarely 611 Sales
## 457 457 31 No Travel_Rarely 1112 Sales
## 458 458 51 No Travel_Rarely 632 Sales
## 459 459 56 No Travel_Rarely 1255 Research & Development
## 460 460 36 No Travel_Rarely 172 Research & Development
## 461 461 42 No Travel_Frequently 1474 Research & Development
## 462 462 36 No Travel_Rarely 311 Research & Development
## 463 463 50 No Travel_Frequently 809 Sales
## 464 464 31 No Travel_Frequently 163 Research & Development
## 465 465 43 Yes Travel_Frequently 807 Research & Development
## 466 466 38 No Travel_Rarely 371 Research & Development
## 467 467 36 No Travel_Rarely 1157 Sales
## 468 468 36 No Travel_Rarely 132 Research & Development
## 469 469 26 Yes Non-Travel 265 Sales
## 470 470 53 No Travel_Rarely 1084 Research & Development
## 471 471 27 No Travel_Rarely 1354 Research & Development
## 472 472 29 Yes Travel_Frequently 459 Research & Development
## 473 473 45 No Travel_Rarely 193 Research & Development
## 474 474 45 No Travel_Rarely 1316 Research & Development
## 475 475 28 No Travel_Rarely 895 Research & Development
## 476 476 46 No Travel_Frequently 1211 Sales
## 477 477 37 No Travel_Rarely 342 Sales
## 478 478 26 No Travel_Rarely 775 Sales
## 479 479 52 No Travel_Rarely 258 Research & Development
## 480 480 37 No Travel_Rarely 161 Research & Development
## 481 481 29 No Travel_Rarely 136 Research & Development
## 482 482 31 No Travel_Rarely 1222 Research & Development
## 483 483 34 No Travel_Frequently 735 Research & Development
## 484 484 56 No Travel_Rarely 713 Research & Development
## 485 485 58 Yes Travel_Rarely 147 Research & Development
## 486 486 41 Yes Travel_Rarely 1102 Sales
## 487 487 30 Yes Travel_Rarely 945 Sales
## 488 488 31 No Travel_Rarely 670 Research & Development
## 489 489 48 Yes Travel_Rarely 626 Research & Development
## 490 490 34 No Travel_Rarely 970 Research & Development
## 491 491 49 No Travel_Frequently 636 Research & Development
## 492 492 50 No Travel_Rarely 1322 Research & Development
## 493 493 29 No Travel_Rarely 469 Sales
## 494 494 35 No Travel_Rarely 1029 Research & Development
## 495 495 36 No Travel_Rarely 1266 Sales
## 496 496 51 No Travel_Rarely 1318 Sales
## 497 497 45 No Travel_Rarely 192 Research & Development
## 498 498 37 No Travel_Frequently 889 Research & Development
## 499 499 36 No Travel_Rarely 1299 Research & Development
## 500 500 39 No Non-Travel 1251 Sales
## 501 501 58 Yes Travel_Rarely 601 Research & Development
## 502 502 28 Yes Travel_Rarely 103 Research & Development
## 503 503 48 Yes Travel_Frequently 708 Sales
## 504 504 29 No Travel_Rarely 1389 Research & Development
## 505 505 47 No Travel_Rarely 359 Research & Development
## 506 506 32 No Travel_Rarely 824 Research & Development
## 507 507 33 Yes Travel_Rarely 350 Sales
## 508 508 51 No Travel_Frequently 541 Sales
## 509 509 37 No Travel_Frequently 319 Sales
## 510 510 41 Yes Travel_Rarely 1360 Research & Development
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## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18
## 1 Divorced 4403 9250 2 Y
## 2 Single 19626 17544 1 Y
## 3 Single 9362 19944 2 Y
## 4 Married 10422 24032 1 Y
## 5 Single 3760 17218 1 Y
## 6 Divorced 8793 4809 1 Y
## 7 Married 2127 5561 2 Y
## 8 Divorced 6694 24223 2 Y
## 9 Married 2220 18410 1 Y
## 10 Married 5063 15332 1 Y
## 11 Married 19392 22539 7 Y
## 12 Divorced 2706 10494 1 Y
## 13 Single 10221 18869 3 Y
## 14 Married 2476 17434 1 Y
## 15 Single 3423 22957 6 Y
## 16 Married 6932 24406 1 Y
## 17 Married 6725 13554 1 Y
## 18 Married 5258 16044 2 Y
## 19 Married 2932 5586 0 Y
## 20 Married 5033 9364 2 Y
## 21 Divorced 8847 13934 2 Y
## 22 Married 8120 18597 3 Y
## 23 Divorced 5679 19627 3 Y
## 24 Single 5332 21602 7 Y
## 25 Single 6949 12291 0 Y
## 26 Married 18213 8751 7 Y
## 27 Married 9924 12355 0 Y
## 28 Single 8224 18385 0 Y
## 29 Single 10448 5843 6 Y
## 30 Married 4258 26589 0 Y
## 31 Divorced 4668 22812 0 Y
## 32 Married 2028 13637 1 Y
## 33 Married 16872 14977 3 Y
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## 36 Married 2794 26062 1 Y
## 37 Married 10306 21530 9 Y
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## 39 Single 19717 4022 6 Y
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## 41 Divorced 16291 22577 4 Y
## 42 Single 6632 12388 0 Y
## 43 Divorced 2587 24941 4 Y
## 44 Single 19613 26362 8 Y
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## 46 Single 3673 16458 1 Y
## 47 Single 6735 12147 6 Y
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## 49 Single 1878 8059 1 Y
## 50 Divorced 2024 5970 6 Y
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## 57 Divorced 2759 20366 6 Y
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## 59 Divorced 13603 11677 2 Y
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## 77 Married 7083 12288 1 Y
## 78 Single 17567 3156 1 Y
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## 87 Single 4960 11825 2 Y
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## 92 Married 7336 11162 1 Y
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## 95 Single 19926 17053 3 Y
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## 107 Married 4319 26283 1 Y
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## 113 Single 10445 15322 7 Y
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## 117 Single 4950 20623 0 Y
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## 124 Single 4028 7791 0 Y
## 125 Single 4554 20260 1 Y
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## 131 Divorced 4187 3356 1 Y
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## 137 Married 2319 6689 1 Y
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## 143 Divorced 10845 24208 6 Y
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## 147 Married 2756 4673 1 Y
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## 150 Divorced 4877 20460 0 Y
## 151 Divorced 4233 11512 2 Y
## 152 Single 5396 21703 1 Y
## 153 Married 5484 16985 1 Y
## 154 Single 6870 15530 3 Y
## 155 Divorced 2008 6896 1 Y
## 156 Married 4736 6069 7 Y
## 157 Married 5677 4258 3 Y
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## 159 Married 10820 11535 8 Y
## 160 Married 13191 23281 3 Y
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## 164 Single 6545 23016 3 Y
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## 169 Divorced 5577 22087 3 Y
## 170 Divorced 2514 26968 4 Y
## 171 Divorced 5304 25275 7 Y
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## 189 Divorced 19144 15815 3 Y
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## 220 Single 2875 9973 1 Y
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## 225 Divorced 4771 14293 2 Y
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## 228 Divorced 2328 12392 1 Y
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## 234 Married 5940 17011 2 Y
## 235 Married 16959 19494 1 Y
## 236 Married 10609 14922 5 Y
## 237 Married 18880 17312 5 Y
## 238 Married 6322 18089 1 Y
## 239 Married 2187 19655 4 Y
## 240 Divorced 13269 21981 5 Y
## 241 Married 7406 6950 1 Y
## 242 Single 18061 13035 3 Y
## 243 Married 2703 4956 0 Y
## 244 Married 4400 15182 3 Y
## 245 Married 5056 17689 1 Y
## 246 Divorced 2532 6054 6 Y
## 247 Single 7143 25713 1 Y
## 248 Married 2269 18024 0 Y
## 249 Divorced 5467 13953 3 Y
## 250 Married 16595 5626 7 Y
## 251 Divorced 2008 20439 1 Y
## 252 Divorced 5079 25952 4 Y
## 253 Single 2274 6153 1 Y
## 254 Married 2956 21495 0 Y
## 255 Divorced 13757 25178 2 Y
## 256 Single 2438 24978 4 Y
## 257 Divorced 2789 3909 1 Y
## 258 Divorced 4779 12761 7 Y
## 259 Married 9713 24444 2 Y
## 260 Divorced 3977 7298 6 Y
## 261 Single 2785 11882 7 Y
## 262 Single 2362 7568 6 Y
## 263 Married 2436 22149 5 Y
## 264 Married 2157 18203 1 Y
## 265 Married 2774 13257 0 Y
## 266 Married 2426 16479 0 Y
## 267 Divorced 17007 11929 7 Y
## 268 Single 1393 24852 1 Y
## 269 Divorced 5968 18079 1 Y
## 270 Married 9957 9096 0 Y
## 271 Married 5154 19665 4 Y
## 272 Divorced 5433 19332 1 Y
## 273 Married 3540 7018 1 Y
## 274 Married 5647 13494 4 Y
## 275 Single 18722 13339 8 Y
## 276 Divorced 19943 18575 4 Y
## 277 Married 2244 24440 1 Y
## 278 Single 2455 10675 0 Y
## 279 Single 5744 26959 1 Y
## 280 Married 4035 16143 0 Y
## 281 Married 7847 6069 1 Y
## 282 Single 4721 3119 2 Y
## 283 Divorced 6513 9060 4 Y
## 284 Divorced 7547 7143 4 Y
## 285 Married 2844 6004 1 Y
## 286 Married 5207 22949 1 Y
## 287 Single 2472 26092 1 Y
## 288 Divorced 2661 8758 0 Y
## 289 Married 9738 22952 0 Y
## 290 Married 2572 20317 1 Y
## 291 Divorced 2270 11005 3 Y
## 292 Married 5674 6927 7 Y
## 293 Single 2279 11781 1 Y
## 294 Married 11957 17231 0 Y
## 295 Single 16437 17381 1 Y
## 296 Married 2660 20232 7 Y
## 297 Married 9547 14074 1 Y
## 298 Single 6074 22887 1 Y
## 299 Single 2321 10322 0 Y
## 300 Married 3464 24737 5 Y
## 301 Married 6524 8891 1 Y
## 302 Married 9069 11031 1 Y
## 303 Divorced 3537 23737 5 Y
## 304 Single 5098 18698 1 Y
## 305 Single 2479 26227 4 Y
## 306 Married 3162 10778 0 Y
## 307 Married 2380 20165 4 Y
## 308 Married 2979 22478 3 Y
## 309 Married 6385 12992 3 Y
## 310 Married 4851 15678 0 Y
## 311 Married 5484 13008 9 Y
## 312 Single 13237 20364 7 Y
## 313 Single 4084 4156 1 Y
## 314 Married 6132 13983 2 Y
## 315 Single 5405 11924 8 Y
## 316 Single 4163 8571 1 Y
## 317 Married 2231 11314 6 Y
## 318 Single 4617 14120 1 Y
## 319 Married 6120 3567 3 Y
## 320 Single 2994 21221 1 Y
## 321 Married 2332 3974 6 Y
## 322 Married 2691 7660 1 Y
## 323 Divorced 3761 2373 9 Y
## 324 Divorced 4615 21029 8 Y
## 325 Married 12742 7060 1 Y
## 326 Single 2318 17808 1 Y
## 327 Divorced 19049 3549 0 Y
## 328 Divorced 5410 11189 6 Y
## 329 Divorced 2133 18115 1 Y
## 330 Married 16799 16616 0 Y
## 331 Single 15379 22384 4 Y
## 332 Married 10400 25812 1 Y
## 333 Divorced 2342 11092 1 Y
## 334 Married 7412 6009 1 Y
## 335 Married 18844 21922 9 Y
## 336 Single 4508 3129 1 Y
## 337 Divorced 4968 18500 1 Y
## 338 Single 7756 22266 0 Y
## 339 Single 4728 17251 3 Y
## 340 Divorced 2341 19715 1 Y
## 341 Married 3419 13072 9 Y
## 342 Married 3867 14222 1 Y
## 343 Divorced 7119 21214 4 Y
## 344 Divorced 2083 22653 1 Y
## 345 Divorced 4115 13192 8 Y
## 346 Single 13341 25098 0 Y
## 347 Single 4033 15834 2 Y
## 348 Divorced 6397 26767 1 Y
## 349 Single 2290 4279 2 Y
## 350 Married 11103 20420 7 Y
## 351 Married 13348 14842 9 Y
## 352 Married 6929 12241 4 Y
## 353 Married 4422 21203 3 Y
## 354 Divorced 4069 8841 3 Y
## 355 Divorced 10447 26458 0 Y
## 356 Single 4759 15891 3 Y
## 357 Married 2368 23300 1 Y
## 358 Married 2546 18300 5 Y
## 359 Married 6162 10877 1 Y
## 360 Married 3210 20251 0 Y
## 361 Married 19999 5678 0 Y
## 362 Single 1091 10642 1 Y
## 363 Married 19845 25846 1 Y
## 364 Divorced 19436 5949 0 Y
## 365 Married 19187 6992 4 Y
## 366 Married 13212 18256 9 Y
## 367 Divorced 4444 22534 4 Y
## 368 Married 5473 19345 0 Y
## 369 Married 2073 12826 2 Y
## 370 Single 5055 10557 7 Y
## 371 Married 4148 11275 1 Y
## 372 Married 8412 2890 0 Y
## 373 Divorced 5210 20308 1 Y
## 374 Married 3936 9953 1 Y
## 375 Single 2728 21082 1 Y
## 376 Single 14411 24450 1 Y
## 377 Single 2743 8269 1 Y
## 378 Divorced 5087 2900 3 Y
## 379 Married 2168 26933 0 Y
## 380 Married 2132 11539 4 Y
## 381 Married 18300 16375 4 Y
## 382 Married 2942 8916 1 Y
## 383 Divorced 5486 24795 4 Y
## 384 Divorced 2322 9518 3 Y
## 385 Married 2461 10332 9 Y
## 386 Married 13582 16292 1 Y
## 387 Single 8789 9096 1 Y
## 388 Divorced 4262 22645 4 Y
## 389 Single 4502 7439 3 Y
## 390 Single 2174 9150 1 Y
## 391 Married 6500 13305 5 Y
## 392 Married 4157 21436 7 Y
## 393 Divorced 3065 3995 1 Y
## 394 Married 10266 2845 4 Y
## 395 Single 2515 9068 5 Y
## 396 Divorced 2695 7747 0 Y
## 397 Married 17861 2288 6 Y
## 398 Married 2742 3072 1 Y
## 399 Divorced 6306 26236 1 Y
## 400 Single 10209 19719 5 Y
## 401 Single 6091 24793 2 Y
## 402 Divorced 5265 16439 2 Y
## 403 Single 2105 5411 4 Y
## 404 Married 8008 22792 4 Y
## 405 Single 2559 17852 1 Y
## 406 Divorced 16627 2671 4 Y
## 407 Married 5468 13402 1 Y
## 408 Divorced 2741 6865 1 Y
## 409 Married 15427 22021 2 Y
## 410 Married 17159 5200 6 Y
## 411 Married 2342 8635 0 Y
## 412 Single 9241 15869 1 Y
## 413 Married 3022 10227 4 Y
## 414 Single 4648 26075 8 Y
## 415 Divorced 2351 12253 0 Y
## 416 Divorced 5762 24442 2 Y
## 417 Single 2564 7181 0 Y
## 418 Married 3688 7122 4 Y
## 419 Married 6474 9961 1 Y
## 420 Married 11935 21526 1 Y
## 421 Married 4325 17736 1 Y
## 422 Married 4449 23866 3 Y
## 423 Married 6172 20739 4 Y
## 424 Single 1611 19305 1 Y
## 425 Married 4724 24232 1 Y
## 426 Divorced 10798 5268 5 Y
## 427 Divorced 1274 7152 1 Y
## 428 Single 3230 10531 1 Y
## 429 Single 3730 9571 0 Y
## 430 Married 5957 23687 6 Y
## 431 Single 4478 5242 1 Y
## 432 Divorced 3544 8532 9 Y
## 433 Single 4284 22710 3 Y
## 434 Divorced 2109 10007 1 Y
## 435 Divorced 9705 20652 2 Y
## 436 Married 6214 3415 1 Y
## 437 Single 4382 16374 6 Y
## 438 Single 2867 20006 0 Y
## 439 Married 2683 3810 1 Y
## 440 Single 7625 19383 0 Y
## 441 Single 4998 2338 4 Y
## 442 Married 2974 25412 9 Y
## 443 Single 3485 14935 2 Y
## 444 Divorced 3195 4167 4 Y
## 445 Divorced 19328 14218 7 Y
## 446 Married 3420 10205 7 Y
## 447 Married 4735 9867 7 Y
## 448 Married 5538 5696 5 Y
## 449 Single 2406 5456 1 Y
## 450 Married 4385 24162 1 Y
## 451 Married 4878 21653 0 Y
## 452 Single 3420 21158 1 Y
## 453 Single 5813 13492 1 Y
## 454 Single 5487 10410 1 Y
## 455 Married 15402 17997 7 Y
## 456 Single 19833 4349 1 Y
## 457 Married 5476 22589 1 Y
## 458 Single 5441 8423 0 Y
## 459 Married 2066 10494 2 Y
## 460 Single 5810 22604 1 Y
## 461 Married 2093 9260 4 Y
## 462 Single 2013 10950 2 Y
## 463 Single 9208 6645 4 Y
## 464 Single 5238 6670 2 Y
## 465 Married 2437 15587 9 Y
## 466 Single 3944 4306 5 Y
## 467 Single 2644 17001 3 Y
## 468 Married 3038 22002 3 Y
## 469 Single 4969 21813 8 Y
## 470 Divorced 4450 26250 1 Y
## 471 Married 2226 6073 1 Y
## 472 Single 2439 14753 1 Y
## 473 Married 13245 15067 4 Y
## 474 Single 3452 9752 5 Y
## 475 Divorced 2207 22482 1 Y
## 476 Single 5772 20445 4 Y
## 477 Divorced 6334 24558 4 Y
## 478 Divorced 4306 4267 5 Y
## 479 Married 2950 17363 9 Y
## 480 Married 13744 15471 1 Y
## 481 Married 5373 6225 0 Y
## 482 Married 2356 14871 3 Y
## 483 Married 5747 26496 1 Y
## 484 Divorced 4257 13939 4 Y
## 485 Married 10312 3465 1 Y
## 486 Single 5993 19479 8 Y
## 487 Single 1081 16019 1 Y
## 488 Divorced 2911 15170 1 Y
## 489 Single 5381 19294 9 Y
## 490 Single 6142 7360 3 Y
## 491 Single 18665 25594 9 Y
## 492 Married 16880 22422 4 Y
## 493 Single 5869 23413 9 Y
## 494 Single 8606 21195 1 Y
## 495 Married 5673 6060 1 Y
## 496 Married 16307 5594 2 Y
## 497 Married 2654 9655 3 Y
## 498 Married 2326 11411 1 Y
## 499 Married 5237 16577 6 Y
## 500 Married 5736 3987 6 Y
## 501 Married 10008 12023 7 Y
## 502 Single 2028 12947 5 Y
## 503 Married 2655 11740 2 Y
## 504 Divorced 9980 10195 1 Y
## 505 Married 17169 26703 3 Y
## 506 Married 5878 15624 3 Y
## 507 Single 2851 9150 1 Y
## 508 Married 10596 15395 2 Y
## 509 Divorced 2793 2539 4 Y
## 510 Married 19545 16280 1 Y
## 511 Married 5343 5982 1 Y
## 512 Married 10920 3449 3 Y
## 513 Married 3433 17360 6 Y
## 514 Single 13458 15146 1 Y
## 515 Single 2610 2851 1 Y
## 516 Divorced 5482 16321 5 Y
## 517 Married 2413 18798 1 Y
## 518 Divorced 2766 8952 8 Y
## 519 Married 4627 23631 0 Y
## 520 Single 2587 10261 1 Y
## 521 Single 2553 8306 1 Y
## 522 Married 2408 7324 1 Y
## 523 Divorced 9724 18787 2 Y
## 524 Divorced 19502 2125 1 Y
## 525 Married 2177 8456 3 Y
## 526 Married 7525 23537 2 Y
## 527 Divorced 5006 6319 4 Y
## 528 Married 6833 17089 1 Y
## 529 Single 2859 26278 1 Y
## 530 Married 4963 4510 9 Y
## 531 Married 8823 24608 0 Y
## 532 Single 6815 21447 6 Y
## 533 Married 2700 23779 1 Y
## 534 Divorced 6667 16542 5 Y
## 535 Married 2119 4759 1 Y
## 536 Divorced 2089 5228 4 Y
## 537 Single 7351 20619 7 Y
## 538 Divorced 2696 24017 0 Y
## 539 Single 3202 21972 1 Y
## 540 Single 5155 2253 7 Y
## 541 Divorced 3491 11309 1 Y
## 542 Married 3143 6076 6 Y
## 543 Divorced 2566 25326 1 Y
## 544 Married 6553 7259 9 Y
## 545 Single 4381 7530 1 Y
## 546 Married 2929 20338 1 Y
## 547 Married 7403 22477 4 Y
## 548 Single 5094 11983 6 Y
## 549 Married 12061 26707 3 Y
## 550 Single 2177 8318 1 Y
## 551 Married 19189 19562 1 Y
## 552 Divorced 4936 23965 1 Y
## 553 Single 3886 4223 1 Y
## 554 Married 2259 5543 4 Y
## 555 Married 4876 14242 9 Y
## 556 Married 9602 3010 4 Y
## 557 Single 3780 23428 7 Y
## 558 Married 3622 22794 1 Y
## 559 Married 4434 11806 1 Y
## 560 Married 5296 20156 1 Y
## 561 Single 2088 15062 4 Y
## 562 Married 15787 21624 2 Y
## 563 Married 2811 12086 9 Y
## 564 Married 11031 26862 4 Y
## 565 Married 3833 24375 3 Y
## 566 Single 2436 13422 6 Y
## 567 Single 2176 19737 4 Y
## 568 Married 9714 5323 1 Y
## 569 Single 6244 7824 7 Y
## 570 Married 13973 4161 3 Y
## 571 Married 4393 26841 5 Y
## 572 Single 2693 8870 1 Y
## 573 Married 6834 19255 1 Y
## 574 Married 6799 22128 1 Y
## 575 Married 6272 7428 1 Y
## 576 Divorced 5993 2689 1 Y
## 577 Single 2858 11473 4 Y
## 578 Single 3162 7973 3 Y
## 579 Single 5042 3140 0 Y
## 580 Divorced 6623 4204 1 Y
## 581 Married 3669 9075 3 Y
## 582 Single 15992 15901 2 Y
## 583 Married 5811 24539 3 Y
## 584 Married 4779 3698 1 Y
## 585 Married 6465 19121 2 Y
## 586 Married 2741 16523 8 Y
## 587 Divorced 5329 15717 7 Y
## 588 Single 3407 6986 7 Y
## 589 Single 3629 19106 4 Y
## 590 Married 4014 16002 3 Y
## 591 Married 2814 10293 1 Y
## 592 Single 4599 7815 0 Y
## 593 Married 2909 15747 3 Y
## 594 Married 3211 22102 1 Y
## 595 Married 10435 25800 1 Y
## 596 Divorced 4037 21816 1 Y
## 597 Married 10903 9129 3 Y
## 598 Married 3448 13436 6 Y
## 599 Married 6142 5174 1 Y
## 600 Married 2293 3020 2 Y
## 601 Married 2033 7103 1 Y
## 602 Married 4087 25174 4 Y
## 603 Married 11416 17802 0 Y
## 604 Married 3131 26342 1 Y
## 605 Single 2760 14630 1 Y
## 606 Married 14814 13514 3 Y
## 607 Single 5473 24668 7 Y
## 608 Divorced 5605 19191 1 Y
## 609 Single 3578 23577 0 Y
## 610 Single 2342 21437 0 Y
## 611 Married 5363 10846 0 Y
## 612 Single 6811 2112 2 Y
## 613 Married 2707 21509 7 Y
## 614 Single 2121 9947 1 Y
## 615 Divorced 7264 9977 5 Y
## 616 Married 3986 11912 1 Y
## 617 Married 3838 8192 8 Y
## 618 Divorced 5454 4009 5 Y
## 619 Married 9991 21457 4 Y
## 620 Married 1281 16900 1 Y
## 621 Single 2926 19783 1 Y
## 622 Divorced 5067 6759 1 Y
## 623 Single 3280 13551 2 Y
## 624 Single 8837 16642 1 Y
## 625 Single 2561 5355 7 Y
## 626 Divorced 2517 3208 1 Y
## 627 Married 4162 15211 1 Y
## 628 Divorced 11510 15682 0 Y
## 629 Married 10976 15813 3 Y
## 630 Single 8621 17654 1 Y
## 631 Married 2662 7975 8 Y
## 632 Single 9888 6770 1 Y
## 633 Single 6861 4981 8 Y
## 634 Divorced 2835 2561 5 Y
## 635 Married 9985 9262 8 Y
## 636 Married 2376 26537 1 Y
## 637 Married 17665 14399 0 Y
## 638 Divorced 8628 22914 1 Y
## 639 Married 2725 21630 1 Y
## 640 Divorced 3041 16346 0 Y
## 641 Divorced 2693 13335 1 Y
## 642 Divorced 4448 10748 2 Y
## 643 Single 16756 17323 7 Y
## 644 Married 2307 14460 1 Y
## 645 Divorced 2107 20293 6 Y
## 646 Married 10855 8552 7 Y
## 647 Single 1904 13556 1 Y
## 648 Married 2305 6217 1 Y
## 649 Single 4312 23016 0 Y
## 650 Single 2166 3339 3 Y
## 651 Single 2450 21731 1 Y
## 652 Divorced 5220 10893 0 Y
## 653 Single 13499 13782 9 Y
## 654 Married 12504 23978 3 Y
## 655 Single 3894 9129 5 Y
## 656 Married 4741 22722 1 Y
## 657 Married 12490 15736 5 Y
## 658 Divorced 16032 24456 3 Y
## 659 Single 6032 10110 6 Y
## 660 Single 6499 22656 1 Y
## 661 Married 5309 21146 1 Y
## 662 Divorced 2768 8416 3 Y
## 663 Divorced 10368 5596 4 Y
## 664 Divorced 3917 9541 1 Y
## 665 Single 4568 10034 0 Y
## 666 Single 6294 23060 8 Y
## 667 Married 7918 6599 1 Y
## 668 Divorced 3408 6705 7 Y
## 669 Married 4883 22845 1 Y
## 670 Married 17779 23474 3 Y
## 671 Single 13664 25258 4 Y
## 672 Divorced 5505 3921 1 Y
## 673 Single 4898 11827 0 Y
## 674 Divorced 2022 16612 1 Y
## 675 Married 3517 22490 7 Y
## 676 Married 18789 9946 2 Y
## 677 Married 5228 23361 0 Y
## 678 Married 6323 26849 1 Y
## 679 Divorced 5131 9192 7 Y
## 680 Married 2329 11737 3 Y
## 681 Married 4256 18154 1 Y
## 682 Married 2258 15238 7 Y
## 683 Married 5347 7419 6 Y
## 684 Divorced 9439 23402 3 Y
## 685 Married 2713 6672 1 Y
## 686 Divorced 10648 14394 1 Y
## 687 Single 7553 22930 0 Y
## 688 Married 2622 13248 6 Y
## 689 Single 1102 9241 1 Y
## 690 Divorced 2592 7129 5 Y
## 691 Married 6077 14814 3 Y
## 692 Married 11849 10268 1 Y
## 693 Single 6781 17078 3 Y
## 694 Single 2285 3427 9 Y
## 695 Divorced 9525 7677 1 Y
## 696 Single 3294 13137 1 Y
## 697 Married 15202 5602 2 Y
## 698 Single 5914 9945 8 Y
## 699 Single 1569 18420 1 Y
## 700 Married 19081 10849 5 Y
## 701 Married 2804 15434 1 Y
## 702 Married 5714 5829 1 Y
## 703 Married 10377 13755 4 Y
## 704 Married 2148 6889 0 Y
## 705 Married 2267 25657 8 Y
## 706 Single 6230 13430 7 Y
## 707 Married 6854 15696 4 Y
## 708 Single 2109 24609 9 Y
## 709 Married 3702 16376 1 Y
## 710 Divorced 17924 4544 1 Y
## 711 Married 11916 25927 1 Y
## 712 Divorced 14118 22102 3 Y
## 713 Single 2559 16620 5 Y
## 714 Married 9699 7246 4 Y
## 715 Single 7587 14229 1 Y
## 716 Married 1555 11585 1 Y
## 717 Married 6578 2706 1 Y
## 718 Single 3660 7909 3 Y
## 719 Single 4581 10414 3 Y
## 720 Divorced 7655 8039 0 Y
## 721 Married 9434 9606 1 Y
## 722 Married 5768 26493 3 Y
## 723 Divorced 2996 20284 5 Y
## 724 Single 8858 15669 0 Y
## 725 Single 3968 13624 4 Y
## 726 Married 4898 7505 0 Y
## 727 Married 2973 21222 5 Y
## 728 Divorced 3815 5972 1 Y
## 729 Single 2340 23213 1 Y
## 730 Married 2523 19299 0 Y
## 731 Divorced 4765 23814 4 Y
## 732 Married 5238 17778 4 Y
## 733 Married 2863 19555 1 Y
## 734 Married 4707 23914 8 Y
## 735 Single 2972 22061 1 Y
## 736 Single 6500 26997 0 Y
## 737 Married 12031 8828 0 Y
## 738 Single 5171 16490 5 Y
## 739 Married 4601 6179 1 Y
## 740 Single 3681 14004 4 Y
## 741 Married 8639 24835 2 Y
## 742 Divorced 11557 25291 9 Y
## 743 Married 4285 3031 1 Y
## 744 Single 3295 12862 1 Y
## 745 Divorced 4907 13684 0 Y
## 746 Divorced 2296 10036 0 Y
## 747 Divorced 2335 3157 4 Y
## 748 Single 5343 25755 0 Y
## 749 Divorced 3907 3622 1 Y
## 750 Single 2500 10515 0 Y
## 751 Single 2323 17205 1 Y
## 752 Single 2302 8319 1 Y
## 753 Married 11245 20689 2 Y
## 754 Married 7314 14011 5 Y
## 755 Married 13549 24001 9 Y
## 756 Single 3708 2104 2 Y
## 757 Married 4014 19170 1 Y
## 758 Married 6651 21534 2 Y
## 759 Married 3306 26176 7 Y
## 760 Single 18824 2493 2 Y
## 761 Single 2404 11479 6 Y
## 762 Married 16752 12982 1 Y
## 763 Single 6151 22074 1 Y
## 764 Married 6347 24920 0 Y
## 765 Married 3737 2243 0 Y
## 766 Single 5769 23447 1 Y
## 767 Married 3376 18863 1 Y
## 768 Divorced 10932 11373 3 Y
## 769 Divorced 7379 17433 2 Y
## 770 Married 5593 17970 1 Y
## 771 Single 4682 4317 3 Y
## 772 Divorced 10453 2137 1 Y
## 773 Single 10938 6420 0 Y
## 774 Married 3388 21777 0 Y
## 775 Married 10124 18611 2 Y
## 776 Single 3505 19630 1 Y
## 777 Divorced 2064 15428 0 Y
## 778 Divorced 2827 14947 1 Y
## 779 Single 2570 11925 1 Y
## 780 Married 4787 26124 9 Y
## 781 Single 3140 21728 1 Y
## 782 Single 4553 20978 1 Y
## 783 Divorced 2277 22650 3 Y
## 784 Married 4539 4905 1 Y
## 785 Married 4978 3536 7 Y
## 786 Single 16422 8847 3 Y
## 787 Married 13116 22984 2 Y
## 788 Married 4941 17747 2 Y
## 789 Married 19038 19805 8 Y
## 790 Single 2096 18830 1 Y
## 791 Married 4789 23070 4 Y
## 792 Single 2311 5711 2 Y
## 793 Married 7756 14199 3 Y
## 794 Divorced 10096 8202 1 Y
## 795 Divorced 6804 23793 1 Y
## 796 Single 3348 3164 1 Y
## 797 Married 13496 7501 0 Y
## 798 Married 2398 15999 1 Y
## 799 Single 1859 6148 1 Y
## 800 Divorced 6842 26308 6 Y
## 801 Married 5376 3193 2 Y
## 802 Single 6582 8346 4 Y
## 803 Married 17068 5355 1 Y
## 804 Single 9582 10333 0 Y
## 805 Married 2042 25043 4 Y
## 806 Married 8321 25949 7 Y
## 807 Married 6232 12477 2 Y
## 808 Married 2323 11992 1 Y
## 809 Married 2862 3811 1 Y
## 810 Married 3058 13364 0 Y
## 811 Divorced 3072 11012 1 Y
## 812 Married 3229 4910 4 Y
## 813 Divorced 4260 5915 1 Y
## 814 Married 19658 5220 3 Y
## 815 Married 2899 12102 0 Y
## 816 Married 2781 6311 0 Y
## 817 Single 6877 20234 5 Y
## 818 Married 11691 25995 0 Y
## 819 Single 2720 11162 0 Y
## 820 Single 9854 23352 3 Y
## 821 Married 3312 18783 3 Y
## 822 Divorced 6931 10732 2 Y
## 823 Married 6712 8978 1 Y
## 824 Married 2610 6233 1 Y
## 825 Married 4663 12421 9 Y
## 826 Divorced 19627 21445 9 Y
## 827 Married 8161 19002 2 Y
## 828 Single 4538 6039 0 Y
## 829 Married 2468 15963 4 Y
## 830 Married 18200 7999 1 Y
## 831 Married 3633 14039 1 Y
## 832 Single 1675 26820 1 Y
## 833 Married 3196 12449 1 Y
## 834 Single 2552 7172 1 Y
## 835 Divorced 13577 25592 1 Y
## 836 Divorced 8834 24666 1 Y
## 837 Married 2996 5182 7 Y
## 838 Single 5337 19921 1 Y
## 839 Divorced 10333 19271 8 Y
## 840 Married 19665 13583 4 Y
## 841 Single 1420 25233 1 Y
## 842 Single 7988 9769 1 Y
## 843 Married 13826 19028 3 Y
## 844 Divorced 8500 5494 0 Y
## 845 Single 6029 25353 5 Y
## 846 Divorced 4558 13535 1 Y
## 847 Single 1483 16102 1 Y
## 848 Divorced 9613 10942 0 Y
## 849 Married 6380 6110 2 Y
## 850 Married 2544 7102 0 Y
## 851 Married 4559 24788 3 Y
## 852 Single 2206 16117 1 Y
## 853 Single 4193 12682 0 Y
## 854 Single 5661 4824 0 Y
## 855 Married 4788 25388 0 Y
## 856 Married 2404 16192 1 Y
## 857 Single 2657 8556 5 Y
## 858 Married 8943 14034 1 Y
## 859 Married 2238 6961 2 Y
## 860 Married 7978 14075 1 Y
## 861 Single 2838 4257 0 Y
## 862 Single 10231 20364 3 Y
## 863 Single 5304 4652 8 Y
## 864 Divorced 13770 10225 9 Y
## 865 Married 9380 14720 4 Y
## 866 Single 7898 18706 1 Y
## 867 Married 4465 12069 0 Y
## 868 Married 4591 24200 3 Y
## 869 Married 4477 20100 4 Y
## 870 Single 4425 15986 5 Y
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 No 11 3 3
## 2 No 14 3 1
## 3 No 11 3 3
## 4 No 19 3 3
## 5 Yes 13 3 3
## 6 No 21 4 3
## 7 Yes 12 3 1
## 8 Yes 14 3 3
## 9 Yes 19 3 4
## 10 No 14 3 2
## 11 No 13 3 4
## 12 No 15 3 2
## 13 No 21 4 2
## 14 No 18 3 1
## 15 No 12 3 3
## 16 No 13 3 4
## 17 No 12 3 3
## 18 No 14 3 3
## 19 Yes 14 3 1
## 20 No 15 3 4
## 21 Yes 11 3 3
## 22 No 12 3 4
## 23 Yes 13 3 2
## 24 No 13 3 4
## 25 No 14 3 1
## 26 No 11 3 3
## 27 No 11 3 4
## 28 Yes 17 3 1
## 29 Yes 13 3 2
## 30 No 18 3 1
## 31 No 17 3 4
## 32 No 18 3 4
## 33 Yes 12 3 2
## 34 No 12 3 2
## 35 No 24 4 2
## 36 No 20 4 3
## 37 No 17 3 3
## 38 Yes 13 3 2
## 39 No 14 3 1
## 40 No 12 3 1
## 41 No 22 4 4
## 42 No 13 3 1
## 43 Yes 16 3 2
## 44 No 22 4 4
## 45 No 22 4 4
## 46 No 13 3 3
## 47 No 15 3 2
## 48 Yes 14 3 2
## 49 Yes 14 3 4
## 50 No 18 3 4
## 51 No 17 3 4
## 52 Yes 21 4 3
## 53 No 13 3 4
## 54 No 12 3 2
## 55 No 23 4 4
## 56 No 20 4 2
## 57 Yes 12 3 4
## 58 Yes 18 3 4
## 59 Yes 18 3 1
## 60 Yes 14 3 4
## 61 Yes 11 3 1
## 62 No 12 3 1
## 63 No 19 3 3
## 64 No 16 3 3
## 65 No 13 3 1
## 66 Yes 12 3 4
## 67 No 13 3 2
## 68 No 13 3 3
## 69 No 13 3 4
## 70 No 12 3 2
## 71 No 22 4 1
## 72 No 18 3 4
## 73 Yes 13 3 1
## 74 Yes 11 3 1
## 75 Yes 19 3 3
## 76 No 23 4 4
## 77 Yes 14 3 4
## 78 No 15 3 2
## 79 No 12 3 4
## 80 Yes 11 3 3
## 81 No 15 3 1
## 82 No 16 3 2
## 83 No 11 3 4
## 84 Yes 13 3 1
## 85 Yes 11 3 4
## 86 Yes 13 3 1
## 87 No 12 3 4
## 88 Yes 24 4 4
## 89 No 15 3 4
## 90 No 12 3 4
## 91 Yes 23 4 3
## 92 No 13 3 1
## 93 Yes 15 3 2
## 94 Yes 16 3 3
## 95 No 15 3 2
## 96 No 25 4 3
## 97 No 12 3 4
## 98 No 14 3 4
## 99 No 13 3 3
## 100 No 17 3 1
## 101 No 14 3 2
## 102 No 18 3 1
## 103 No 15 3 1
## 104 No 16 3 1
## 105 No 12 3 2
## 106 No 22 4 2
## 107 No 13 3 1
## 108 Yes 18 3 1
## 109 No 22 4 4
## 110 No 14 3 4
## 111 Yes 14 3 4
## 112 No 15 3 2
## 113 No 19 3 4
## 114 No 11 3 1
## 115 Yes 15 3 4
## 116 Yes 13 3 4
## 117 No 14 3 2
## 118 Yes 13 3 1
## 119 No 12 3 1
## 120 No 11 3 4
## 121 No 14 3 1
## 122 Yes 11 3 4
## 123 Yes 12 3 3
## 124 No 20 4 1
## 125 No 18 3 1
## 126 Yes 11 3 4
## 127 Yes 22 4 2
## 128 No 13 3 4
## 129 Yes 14 3 1
## 130 No 12 3 4
## 131 Yes 13 3 2
## 132 No 12 3 3
## 133 No 11 3 4
## 134 No 21 4 3
## 135 No 11 3 2
## 136 No 24 4 4
## 137 Yes 11 3 4
## 138 No 11 3 3
## 139 Yes 21 4 4
## 140 No 12 3 1
## 141 Yes 12 3 1
## 142 No 12 3 4
## 143 No 13 3 2
## 144 No 14 3 2
## 145 No 20 4 2
## 146 No 14 3 1
## 147 No 13 3 4
## 148 No 13 3 4
## 149 No 22 4 4
## 150 No 21 4 2
## 151 No 17 3 3
## 152 No 12 3 4
## 153 No 14 3 3
## 154 No 12 3 1
## 155 No 14 3 2
## 156 Yes 12 3 2
## 157 No 14 3 3
## 158 No 11 3 4
## 159 No 11 3 3
## 160 Yes 17 3 3
## 161 Yes 12 3 3
## 162 No 12 3 1
## 163 No 12 3 4
## 164 Yes 13 3 3
## 165 No 15 3 3
## 166 No 13 3 4
## 167 No 16 3 2
## 168 No 19 3 3
## 169 Yes 12 3 2
## 170 No 22 4 1
## 171 No 23 4 4
## 172 No 18 3 3
## 173 No 11 3 4
## 174 Yes 14 3 4
## 175 No 13 3 3
## 176 Yes 11 3 3
## 177 Yes 13 3 4
## 178 Yes 14 3 2
## 179 No 14 3 4
## 180 No 22 4 3
## 181 No 18 3 2
## 182 No 15 3 4
## 183 No 12 3 4
## 184 No 18 3 4
## 185 No 11 3 1
## 186 No 23 4 4
## 187 No 17 3 3
## 188 No 14 3 3
## 189 No 14 3 1
## 190 No 15 3 2
## 191 No 13 3 4
## 192 No 14 3 4
## 193 No 14 3 3
## 194 No 14 3 1
## 195 No 12 3 4
## 196 No 11 3 2
## 197 No 23 4 2
## 198 No 17 3 2
## 199 No 14 3 4
## 200 No 21 4 4
## 201 Yes 16 3 4
## 202 No 17 3 2
## 203 No 12 3 3
## 204 Yes 13 3 4
## 205 No 11 3 1
## 206 Yes 13 3 4
## 207 No 13 3 2
## 208 Yes 19 3 2
## 209 No 14 3 4
## 210 Yes 13 3 3
## 211 No 23 4 3
## 212 No 13 3 2
## 213 No 17 3 1
## 214 Yes 13 3 4
## 215 No 13 3 4
## 216 No 14 3 3
## 217 No 19 3 3
## 218 No 11 3 2
## 219 No 14 3 3
## 220 Yes 20 4 2
## 221 No 22 4 3
## 222 No 14 3 4
## 223 Yes 25 4 3
## 224 No 14 3 4
## 225 No 19 3 4
## 226 No 13 3 3
## 227 No 12 3 3
## 228 Yes 16 3 1
## 229 No 21 4 1
## 230 Yes 19 3 2
## 231 No 19 3 1
## 232 No 13 3 1
## 233 No 11 3 3
## 234 No 14 3 4
## 235 Yes 12 3 4
## 236 No 11 3 3
## 237 No 11 3 1
## 238 Yes 12 3 4
## 239 No 14 3 3
## 240 No 15 3 3
## 241 Yes 21 4 4
## 242 No 22 4 3
## 243 No 23 4 4
## 244 No 12 3 1
## 245 Yes 15 3 3
## 246 No 14 3 3
## 247 Yes 14 3 3
## 248 No 14 3 2
## 249 Yes 14 3 1
## 250 No 16 3 2
## 251 No 12 3 3
## 252 No 13 3 4
## 253 No 14 3 4
## 254 No 17 3 3
## 255 No 11 3 3
## 256 No 13 3 3
## 257 No 11 3 3
## 258 No 14 3 2
## 259 Yes 13 3 4
## 260 Yes 19 3 3
## 261 No 14 3 3
## 262 No 13 3 3
## 263 Yes 13 3 3
## 264 No 15 3 2
## 265 No 12 3 3
## 266 No 13 3 3
## 267 No 14 3 4
## 268 No 12 3 1
## 269 No 20 4 3
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## 272 No 12 3 3
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## 276 No 13 3 4
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## 281 Yes 17 3 1
## 282 Yes 13 3 3
## 283 No 17 3 4
## 284 No 12 3 4
## 285 No 13 3 4
## 286 Yes 12 3 2
## 287 Yes 23 4 1
## 288 No 11 3 3
## 289 No 14 3 3
## 290 No 16 3 2
## 291 No 14 3 4
## 292 No 15 3 3
## 293 No 16 3 4
## 294 No 18 3 1
## 295 Yes 21 4 4
## 296 Yes 11 3 3
## 297 No 17 3 3
## 298 Yes 24 4 4
## 299 Yes 22 4 1
## 300 Yes 13 3 4
## 301 No 14 3 4
## 302 No 22 4 4
## 303 No 12 3 4
## 304 No 19 3 2
## 305 No 24 4 1
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## 307 No 18 3 2
## 308 No 17 3 4
## 309 Yes 14 3 4
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## 311 No 17 3 2
## 312 No 15 3 3
## 313 No 12 3 1
## 314 No 17 3 3
## 315 No 14 3 4
## 316 Yes 17 3 3
## 317 No 18 3 4
## 318 No 12 3 2
## 319 Yes 12 3 4
## 320 Yes 12 3 4
## 321 No 20 4 3
## 322 No 12 3 4
## 323 No 12 3 2
## 324 Yes 23 4 1
## 325 No 16 3 3
## 326 No 19 3 3
## 327 Yes 14 3 4
## 328 Yes 17 3 3
## 329 Yes 16 3 3
## 330 No 14 3 3
## 331 No 14 3 1
## 332 No 11 3 3
## 333 Yes 12 3 3
## 334 No 11 3 4
## 335 No 21 4 4
## 336 No 22 4 2
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## 338 No 17 3 3
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## 340 No 13 3 4
## 341 Yes 14 3 4
## 342 Yes 12 3 2
## 343 No 15 3 3
## 344 No 20 4 3
## 345 No 19 3 3
## 346 No 12 3 1
## 347 No 11 3 4
## 348 No 20 4 1
## 349 No 13 3 4
## 350 No 11 3 3
## 351 No 13 3 2
## 352 No 11 3 2
## 353 Yes 13 3 4
## 354 Yes 18 3 3
## 355 Yes 13 3 4
## 356 No 18 3 4
## 357 No 19 3 3
## 358 No 16 3 2
## 359 Yes 22 4 2
## 360 No 11 3 3
## 361 No 14 3 1
## 362 No 17 3 4
## 363 No 15 3 4
## 364 No 19 3 4
## 365 No 14 3 4
## 366 No 11 3 4
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## 368 No 12 3 4
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## 370 No 16 3 3
## 371 No 12 3 3
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## 373 No 18 3 1
## 374 No 11 3 1
## 375 No 11 3 1
## 376 Yes 13 3 4
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## 378 Yes 12 3 3
## 379 Yes 18 3 1
## 380 Yes 11 3 2
## 381 No 11 3 2
## 382 No 23 4 4
## 383 No 11 3 1
## 384 No 13 3 3
## 385 Yes 12 3 3
## 386 No 13 3 2
## 387 No 14 3 1
## 388 No 12 3 2
## 389 No 15 3 3
## 390 Yes 11 3 3
## 391 No 17 3 2
## 392 Yes 19 3 3
## 393 Yes 13 3 4
## 394 No 19 3 4
## 395 Yes 14 3 4
## 396 Yes 18 3 2
## 397 No 13 3 3
## 398 No 15 3 4
## 399 No 21 4 1
## 400 Yes 18 3 2
## 401 No 20 4 3
## 402 No 16 3 2
## 403 No 12 3 3
## 404 No 12 3 3
## 405 No 11 3 4
## 406 Yes 14 3 3
## 407 No 14 3 1
## 408 No 14 3 3
## 409 No 16 3 3
## 410 No 24 4 3
## 411 No 21 4 3
## 412 No 12 3 2
## 413 No 21 4 1
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## 415 No 16 3 4
## 416 No 14 3 3
## 417 No 14 3 3
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## 419 No 13 3 2
## 420 No 18 3 3
## 421 No 15 3 3
## 422 Yes 15 3 1
## 423 Yes 18 3 2
## 424 No 15 3 3
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## 426 No 13 3 3
## 427 No 13 3 2
## 428 No 17 3 1
## 429 Yes 14 3 4
## 430 No 13 3 2
## 431 Yes 11 3 1
## 432 No 16 3 2
## 433 No 20 4 1
## 434 No 13 3 3
## 435 No 12 3 2
## 436 No 18 3 1
## 437 No 17 3 4
## 438 No 13 3 4
## 439 Yes 14 3 3
## 440 No 13 3 3
## 441 Yes 14 3 4
## 442 No 17 3 3
## 443 No 11 3 3
## 444 Yes 18 3 1
## 445 Yes 17 3 3
## 446 No 12 3 3
## 447 No 15 3 4
## 448 No 18 3 3
## 449 No 11 3 4
## 450 No 15 3 1
## 451 Yes 13 3 1
## 452 No 13 3 3
## 453 Yes 18 3 4
## 454 No 14 3 2
## 455 No 11 3 1
## 456 No 14 3 2
## 457 No 11 3 1
## 458 Yes 22 4 4
## 459 No 22 4 4
## 460 No 16 3 3
## 461 No 17 3 4
## 462 No 11 3 3
## 463 No 11 3 4
## 464 No 20 4 4
## 465 Yes 16 3 4
## 466 Yes 11 3 3
## 467 Yes 21 4 4
## 468 No 12 3 2
## 469 No 18 3 4
## 470 No 11 3 3
## 471 No 11 3 3
## 472 Yes 24 4 2
## 473 Yes 14 3 2
## 474 No 13 3 2
## 475 No 16 3 4
## 476 Yes 21 4 3
## 477 No 19 3 4
## 478 No 12 3 1
## 479 No 13 3 3
## 480 Yes 25 4 1
## 481 No 12 3 1
## 482 Yes 19 3 2
## 483 Yes 15 3 2
## 484 Yes 18 3 3
## 485 No 12 3 4
## 486 Yes 11 3 1
## 487 No 13 3 3
## 488 No 17 3 4
## 489 Yes 13 3 4
## 490 No 11 3 4
## 491 Yes 11 3 4
## 492 Yes 11 3 2
## 493 No 11 3 3
## 494 No 19 3 4
## 495 Yes 13 3 1
## 496 No 14 3 3
## 497 No 21 4 4
## 498 Yes 12 3 3
## 499 No 13 3 2
## 500 No 19 3 3
## 501 Yes 14 3 4
## 502 Yes 14 3 2
## 503 Yes 11 3 3
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## 505 No 19 3 2
## 506 No 12 3 1
## 507 Yes 13 3 2
## 508 No 11 3 2
## 509 No 17 3 3
## 510 No 12 3 4
## 511 No 11 3 3
## 512 No 21 4 2
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## 514 Yes 12 3 3
## 515 No 24 4 3
## 516 No 18 3 4
## 517 Yes 18 3 3
## 518 No 22 4 2
## 519 No 12 3 1
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## 521 No 16 3 3
## 522 Yes 17 3 3
## 523 No 17 3 3
## 524 Yes 17 3 3
## 525 No 17 3 1
## 526 No 12 3 1
## 527 Yes 11 3 1
## 528 Yes 12 3 4
## 529 No 18 3 1
## 530 Yes 18 3 1
## 531 No 18 3 1
## 532 No 13 3 1
## 533 No 24 4 3
## 534 No 18 3 2
## 535 Yes 11 3 4
## 536 No 14 3 4
## 537 No 16 3 3
## 538 Yes 11 3 3
## 539 Yes 16 3 2
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## 541 No 13 3 1
## 542 No 19 3 2
## 543 Yes 15 3 4
## 544 No 14 3 2
## 545 No 11 3 3
## 546 No 12 3 2
## 547 No 11 3 3
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## 550 No 16 3 1
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## 558 Yes 13 3 4
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## 561 No 12 3 3
## 562 Yes 14 3 2
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## 564 No 20 4 3
## 565 No 21 4 3
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## 568 No 11 3 4
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## 571 No 21 4 3
## 572 No 19 3 1
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## 575 No 20 4 4
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## 578 No 14 3 4
## 579 No 13 3 4
## 580 Yes 11 3 2
## 581 No 11 3 3
## 582 No 14 3 2
## 583 Yes 16 3 3
## 584 Yes 20 4 1
## 585 Yes 13 3 4
## 586 Yes 15 3 3
## 587 Yes 12 3 4
## 588 No 23 4 2
## 589 No 18 3 1
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## 591 Yes 14 3 2
## 592 Yes 23 4 3
## 593 No 15 3 4
## 594 No 14 3 4
## 595 No 13 3 4
## 596 No 22 4 1
## 597 No 16 3 1
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## 603 Yes 12 3 3
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## 621 Yes 18 3 2
## 622 Yes 19 3 3
## 623 No 16 3 3
## 624 Yes 16 3 3
## 625 No 11 3 3
## 626 No 11 3 2
## 627 Yes 12 3 3
## 628 Yes 14 3 2
## 629 No 18 3 2
## 630 No 14 3 2
## 631 No 20 4 2
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## 633 Yes 12 3 3
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## 638 No 18 3 3
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## 643 No 15 3 2
## 644 Yes 23 4 2
## 645 No 17 3 1
## 646 No 11 3 1
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## 648 No 15 3 3
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## 651 No 19 3 2
## 652 Yes 18 3 2
## 653 No 17 3 3
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## 655 No 16 3 3
## 656 Yes 13 3 3
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## 658 No 20 4 1
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## 663 Yes 12 3 2
## 664 No 20 4 1
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## 669 No 18 3 1
## 670 No 14 3 1
## 671 No 13 3 1
## 672 No 14 3 3
## 673 No 14 3 4
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## 676 No 14 3 3
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## 678 No 11 3 1
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## 681 No 12 3 1
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## 689 No 22 4 3
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## 704 Yes 11 3 3
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## 706 No 14 3 4
## 707 No 15 3 2
## 708 No 18 3 4
## 709 No 11 3 2
## 710 No 11 3 4
## 711 Yes 23 4 4
## 712 No 12 3 3
## 713 No 11 3 3
## 714 No 11 3 1
## 715 No 15 3 2
## 716 No 11 3 3
## 717 No 18 3 1
## 718 No 13 3 4
## 719 Yes 24 4 1
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## 721 No 15 3 3
## 722 No 17 3 1
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## 724 No 11 3 2
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## 726 No 12 3 4
## 727 No 15 3 2
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## 729 Yes 18 3 2
## 730 No 14 3 3
## 731 No 21 4 3
## 732 Yes 18 3 1
## 733 No 12 3 1
## 734 No 12 3 4
## 735 No 13 3 3
## 736 No 14 3 2
## 737 No 11 3 1
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## 739 No 16 3 2
## 740 No 14 3 4
## 741 No 18 3 4
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## 747 Yes 15 3 4
## 748 No 20 4 3
## 749 No 13 3 2
## 750 No 14 3 1
## 751 Yes 14 3 2
## 752 Yes 11 3 1
## 753 Yes 15 3 3
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## 755 No 12 3 1
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## 757 Yes 25 4 4
## 758 No 14 3 2
## 759 No 19 3 4
## 760 Yes 16 3 1
## 761 Yes 20 4 3
## 762 Yes 11 3 3
## 763 No 13 3 1
## 764 No 12 3 1
## 765 No 19 3 3
## 766 Yes 14 3 1
## 767 No 13 3 3
## 768 No 15 3 3
## 769 No 11 3 3
## 770 No 13 3 4
## 771 No 14 3 3
## 772 No 25 4 3
## 773 No 25 4 4
## 774 Yes 17 3 1
## 775 Yes 14 3 3
## 776 No 18 3 4
## 777 No 21 4 1
## 778 No 12 3 3
## 779 No 20 4 3
## 780 Yes 14 3 2
## 781 Yes 22 4 4
## 782 No 11 3 1
## 783 Yes 11 3 3
## 784 No 12 3 1
## 785 No 11 3 4
## 786 No 11 3 3
## 787 No 11 3 4
## 788 No 15 3 1
## 789 No 12 3 2
## 790 No 18 3 4
## 791 No 25 4 1
## 792 No 15 3 4
## 793 Yes 19 3 4
## 794 No 13 3 2
## 795 Yes 15 3 1
## 796 Yes 11 3 1
## 797 Yes 14 3 2
## 798 Yes 17 3 3
## 799 Yes 25 4 2
## 800 No 20 4 1
## 801 No 19 3 1
## 802 Yes 13 3 3
## 803 Yes 14 3 4
## 804 Yes 22 4 1
## 805 No 12 3 3
## 806 Yes 13 3 4
## 807 No 11 3 2
## 808 No 24 4 1
## 809 No 12 3 2
## 810 Yes 16 3 4
## 811 No 11 3 3
## 812 No 11 3 2
## 813 Yes 12 3 4
## 814 No 11 3 3
## 815 No 19 3 4
## 816 No 13 3 2
## 817 Yes 24 4 2
## 818 No 11 3 4
## 819 No 13 3 4
## 820 Yes 11 3 4
## 821 No 17 3 4
## 822 No 14 3 4
## 823 No 21 4 4
## 824 No 12 3 3
## 825 Yes 12 3 2
## 826 No 17 3 4
## 827 No 13 3 1
## 828 Yes 12 3 4
## 829 No 14 3 2
## 830 No 11 3 3
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## 832 Yes 19 3 4
## 833 No 12 3 3
## 834 No 25 4 3
## 835 Yes 15 3 4
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## 838 No 12 3 4
## 839 Yes 12 3 3
## 840 No 12 3 4
## 841 No 13 3 3
## 842 No 13 3 1
## 843 No 22 4 3
## 844 No 11 3 4
## 845 No 12 3 1
## 846 No 12 3 4
## 847 No 14 3 4
## 848 No 17 3 1
## 849 Yes 12 3 1
## 850 No 18 3 1
## 851 Yes 11 3 3
## 852 No 13 3 1
## 853 Yes 12 3 4
## 854 No 19 3 3
## 855 Yes 11 3 4
## 856 No 13 3 1
## 857 Yes 11 3 3
## 858 No 24 4 1
## 859 No 21 4 4
## 860 No 11 3 4
## 861 No 14 3 2
## 862 No 14 3 4
## 863 Yes 13 3 2
## 864 Yes 12 3 4
## 865 Yes 18 3 4
## 866 No 11 3 3
## 867 No 18 3 1
## 868 Yes 17 3 3
## 869 Yes 19 3 3
## 870 No 11 3 4
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 1 80 1 8 3
## 2 80 0 21 2
## 3 80 0 10 2
## 4 80 2 14 3
## 5 80 0 6 2
## 6 80 2 9 4
## 7 80 0 7 5
## 8 80 3 8 5
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## 17 80 1 8 2
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## 21 80 1 13 2
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## 34 80 1 10 2
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## 38 80 3 16 3
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## 870 1 2#File for Classification Predictions
Case2PredictionsSeanMcWhirter_Attrition=read.csv("C:/Users/seans/Desktop/DDS/Unit 14 and 15 Case Study 2/CaseStudy2CompSet No Attrition.csv", header=TRUE, sep=",", stringsAsFactors = TRUE)
saveRDS(Case2PredictionsSeanMcWhirter_Attrition, file ="Case2PredictionsSeanMcWhirter_Attrition.Rds")
readRDS(Case2PredictionsSeanMcWhirter_Attrition, file ="Case2PredictionsSeanMcWhirter_Attrition.Rds")## ID Age BusinessTravel DailyRate Department
## 1 1171 35 Travel_Rarely 750 Research & Development
## 2 1172 33 Travel_Rarely 147 Human Resources
## 3 1173 26 Travel_Rarely 1330 Research & Development
## 4 1174 55 Travel_Rarely 1311 Research & Development
## 5 1175 29 Travel_Rarely 1246 Sales
## 6 1176 51 Travel_Frequently 1456 Research & Development
## 7 1177 52 Non-Travel 585 Sales
## 8 1178 39 Travel_Rarely 1387 Research & Development
## 9 1179 31 Travel_Rarely 1062 Research & Development
## 10 1180 31 Travel_Frequently 534 Research & Development
## 11 1181 40 Travel_Frequently 1469 Research & Development
## 12 1182 31 Travel_Rarely 1082 Research & Development
## 13 1183 33 Non-Travel 775 Research & Development
## 14 1184 50 Travel_Rarely 691 Research & Development
## 15 1185 33 Travel_Rarely 516 Research & Development
## 16 1186 42 Non-Travel 335 Research & Development
## 17 1187 33 Travel_Rarely 575 Research & Development
## 18 1188 29 Travel_Rarely 694 Research & Development
## 19 1189 55 Travel_Rarely 436 Sales
## 20 1190 49 Travel_Rarely 1184 Sales
## 21 1191 32 Travel_Rarely 267 Research & Development
## 22 1192 50 Travel_Rarely 1126 Research & Development
## 23 1193 54 Travel_Rarely 157 Research & Development
## 24 1194 24 Travel_Frequently 897 Human Resources
## 25 1195 45 Travel_Rarely 1038 Research & Development
## 26 1196 36 Non-Travel 635 Sales
## 27 1197 41 Non-Travel 552 Human Resources
## 28 1198 21 Travel_Rarely 1343 Sales
## 29 1199 36 Travel_Rarely 335 Sales
## 30 1200 30 Travel_Frequently 109 Research & Development
## 31 1201 35 Travel_Frequently 146 Research & Development
## 32 1202 46 Travel_Rarely 717 Research & Development
## 33 1203 39 Travel_Rarely 903 Sales
## 34 1204 35 Travel_Rarely 185 Research & Development
## 35 1205 21 Travel_Rarely 1334 Research & Development
## 36 1206 30 Travel_Rarely 1358 Sales
## 37 1207 42 Travel_Frequently 555 Sales
## 38 1208 45 Travel_Frequently 1297 Research & Development
## 39 1209 39 Travel_Frequently 672 Research & Development
## 40 1210 48 Travel_Rarely 715 Research & Development
## 41 1211 38 Travel_Rarely 364 Research & Development
## 42 1212 21 Travel_Rarely 546 Research & Development
## 43 1213 55 Travel_Rarely 836 Research & Development
## 44 1214 59 Travel_Rarely 1429 Research & Development
## 45 1215 58 Travel_Rarely 1145 Research & Development
## 46 1216 49 Travel_Frequently 1064 Research & Development
## 47 1217 52 Travel_Rarely 1490 Research & Development
## 48 1218 37 Travel_Rarely 589 Sales
## 49 1219 37 Non-Travel 1413 Research & Development
## 50 1220 26 Travel_Frequently 921 Research & Development
## 51 1221 52 Travel_Rarely 621 Sales
## 52 1222 33 Travel_Frequently 1392 Research & Development
## 53 1223 32 Non-Travel 300 Research & Development
## 54 1224 51 Travel_Rarely 1178 Sales
## 55 1225 39 Travel_Rarely 1329 Sales
## 56 1226 39 Travel_Frequently 1218 Research & Development
## 57 1227 33 Travel_Rarely 527 Research & Development
## 58 1228 30 Non-Travel 1400 Research & Development
## 59 1229 30 Travel_Rarely 979 Sales
## 60 1230 44 Travel_Rarely 986 Research & Development
## 61 1231 40 Travel_Rarely 1398 Sales
## 62 1232 31 Travel_Rarely 741 Research & Development
## 63 1233 20 Travel_Rarely 1097 Research & Development
## 64 1234 42 Travel_Frequently 481 Sales
## 65 1235 32 Travel_Rarely 495 Research & Development
## 66 1236 33 Travel_Rarely 118 Sales
## 67 1237 53 Travel_Rarely 102 Research & Development
## 68 1238 33 Travel_Frequently 1111 Sales
## 69 1239 24 Travel_Frequently 535 Sales
## 70 1240 42 Travel_Rarely 603 Research & Development
## 71 1241 35 Travel_Rarely 755 Research & Development
## 72 1242 44 Travel_Rarely 200 Research & Development
## 73 1243 54 Travel_Rarely 376 Research & Development
## 74 1244 37 Travel_Rarely 783 Research & Development
## 75 1245 43 Travel_Frequently 422 Research & Development
## 76 1246 28 Travel_Rarely 640 Research & Development
## 77 1247 57 Travel_Rarely 593 Research & Development
## 78 1248 30 Travel_Frequently 1312 Research & Development
## 79 1249 36 Travel_Frequently 469 Research & Development
## 80 1250 36 Travel_Rarely 1041 Human Resources
## 81 1251 34 Travel_Rarely 285 Research & Development
## 82 1252 34 Travel_Rarely 216 Sales
## 83 1253 41 Travel_Rarely 1411 Research & Development
## 84 1254 34 Travel_Frequently 648 Human Resources
## 85 1255 55 Travel_Rarely 147 Research & Development
## 86 1256 52 Travel_Frequently 322 Research & Development
## 87 1257 34 Travel_Rarely 419 Research & Development
## 88 1258 52 Non-Travel 771 Sales
## 89 1259 49 Travel_Rarely 1261 Research & Development
## 90 1260 26 Travel_Rarely 1146 Sales
## 91 1261 44 Travel_Rarely 921 Research & Development
## 92 1262 39 Non-Travel 792 Research & Development
## 93 1263 27 Travel_Rarely 1054 Research & Development
## 94 1264 50 Travel_Frequently 1115 Research & Development
## 95 1265 40 Travel_Rarely 118 Sales
## 96 1266 39 Travel_Frequently 766 Sales
## 97 1267 60 Travel_Rarely 370 Research & Development
## 98 1268 39 Travel_Rarely 867 Research & Development
## 99 1269 29 Travel_Rarely 992 Research & Development
## 100 1270 31 Travel_Rarely 202 Research & Development
## 101 1271 41 Travel_Rarely 896 Sales
## 102 1272 30 Non-Travel 829 Research & Development
## 103 1273 25 Travel_Rarely 1219 Research & Development
## 104 1274 53 Travel_Rarely 1070 Research & Development
## 105 1275 30 Travel_Frequently 448 Sales
## 106 1276 26 Travel_Rarely 1066 Research & Development
## 107 1277 36 Travel_Frequently 1302 Research & Development
## 108 1278 31 Travel_Rarely 1079 Sales
## 109 1279 37 Travel_Rarely 807 Human Resources
## 110 1280 54 Non-Travel 142 Human Resources
## 111 1281 35 Travel_Frequently 662 Sales
## 112 1282 35 Travel_Rarely 1296 Research & Development
## 113 1283 37 Travel_Rarely 367 Research & Development
## 114 1284 38 Travel_Rarely 1084 Research & Development
## 115 1285 58 Travel_Rarely 605 Sales
## 116 1286 43 Travel_Frequently 775 Sales
## 117 1287 30 Travel_Rarely 1465 Research & Development
## 118 1288 29 Travel_Rarely 121 Sales
## 119 1289 24 Non-Travel 1092 Research & Development
## 120 1290 58 Travel_Rarely 682 Sales
## 121 1291 38 Travel_Rarely 1321 Sales
## 122 1292 52 Travel_Rarely 319 Research & Development
## 123 1293 50 Travel_Rarely 264 Sales
## 124 1294 36 Travel_Frequently 884 Research & Development
## 125 1295 36 Travel_Frequently 607 Sales
## 126 1296 53 Travel_Rarely 868 Sales
## 127 1297 29 Travel_Rarely 1396 Sales
## 128 1298 50 Travel_Rarely 1099 Research & Development
## 129 1299 54 Travel_Rarely 821 Research & Development
## 130 1300 36 Non-Travel 894 Research & Development
## 131 1301 56 Travel_Rarely 310 Research & Development
## 132 1302 44 Travel_Rarely 136 Research & Development
## 133 1303 49 Travel_Rarely 1091 Research & Development
## 134 1304 32 Travel_Rarely 414 Sales
## 135 1305 55 Travel_Rarely 111 Sales
## 136 1306 35 Travel_Rarely 1315 Research & Development
## 137 1307 36 Travel_Rarely 1425 Research & Development
## 138 1308 42 Travel_Rarely 1210 Research & Development
## 139 1309 27 Non-Travel 1277 Research & Development
## 140 1310 46 Non-Travel 849 Sales
## 141 1311 45 Travel_Rarely 194 Research & Development
## 142 1312 42 Travel_Frequently 570 Research & Development
## 143 1313 40 Travel_Rarely 448 Research & Development
## 144 1314 32 Travel_Rarely 120 Research & Development
## 145 1315 45 Travel_Rarely 538 Research & Development
## 146 1316 41 Travel_Frequently 143 Sales
## 147 1317 42 Travel_Rarely 188 Research & Development
## 148 1318 31 Travel_Frequently 667 Sales
## 149 1319 28 Travel_Rarely 1117 Research & Development
## 150 1320 55 Travel_Rarely 685 Sales
## 151 1321 33 Travel_Rarely 832 Research & Development
## 152 1322 53 Travel_Rarely 346 Research & Development
## 153 1323 40 Travel_Frequently 530 Research & Development
## 154 1324 30 Travel_Rarely 1092 Research & Development
## 155 1325 40 Travel_Rarely 804 Research & Development
## 156 1326 31 Travel_Rarely 329 Research & Development
## 157 1327 34 Non-Travel 999 Research & Development
## 158 1328 40 Travel_Rarely 299 Sales
## 159 1329 21 Travel_Rarely 391 Research & Development
## 160 1330 35 Non-Travel 1097 Research & Development
## 161 1331 40 Travel_Rarely 369 Research & Development
## 162 1332 28 Travel_Rarely 1083 Research & Development
## 163 1333 34 Travel_Rarely 699 Research & Development
## 164 1334 29 Travel_Rarely 806 Research & Development
## 165 1335 42 Travel_Rarely 1265 Research & Development
## 166 1336 29 Travel_Rarely 1283 Research & Development
## 167 1337 51 Travel_Frequently 237 Sales
## 168 1338 27 Non-Travel 210 Sales
## 169 1339 50 Travel_Rarely 1207 Research & Development
## 170 1340 35 Travel_Rarely 195 Sales
## 171 1341 25 Travel_Rarely 180 Research & Development
## 172 1342 44 Travel_Rarely 935 Research & Development
## 173 1343 27 Travel_Rarely 975 Research & Development
## 174 1344 38 Travel_Rarely 1380 Research & Development
## 175 1345 27 Travel_Rarely 1469 Research & Development
## 176 1346 49 Travel_Frequently 279 Research & Development
## 177 1347 49 Travel_Rarely 1418 Research & Development
## 178 1348 35 Travel_Rarely 1349 Research & Development
## 179 1349 44 Travel_Rarely 1315 Research & Development
## 180 1350 44 Travel_Rarely 1448 Sales
## 181 1351 26 Travel_Frequently 575 Research & Development
## 182 1352 43 Travel_Rarely 1273 Research & Development
## 183 1353 50 Travel_Rarely 328 Research & Development
## 184 1354 41 Travel_Rarely 930 Sales
## 185 1355 43 Travel_Rarely 982 Research & Development
## 186 1356 53 Travel_Rarely 238 Sales
## 187 1357 45 Travel_Rarely 1005 Research & Development
## 188 1358 20 Travel_Rarely 805 Research & Development
## 189 1359 54 Travel_Frequently 966 Research & Development
## 190 1360 39 Travel_Rarely 524 Research & Development
## 191 1361 30 Travel_Rarely 1334 Sales
## 192 1362 39 Travel_Rarely 170 Research & Development
## 193 1363 35 Travel_Rarely 819 Research & Development
## 194 1364 38 Travel_Frequently 1186 Research & Development
## 195 1365 25 Travel_Rarely 622 Sales
## 196 1366 29 Travel_Rarely 738 Research & Development
## 197 1367 34 Travel_Frequently 988 Human Resources
## 198 1368 31 Non-Travel 979 Research & Development
## 199 1369 51 Travel_Rarely 770 Human Resources
## 200 1370 54 Travel_Rarely 155 Research & Development
## 201 1371 41 Travel_Frequently 840 Research & Development
## 202 1372 28 Travel_Rarely 1181 Research & Development
## 203 1373 39 Travel_Rarely 1431 Research & Development
## 204 1374 36 Travel_Rarely 363 Research & Development
## 205 1375 53 Non-Travel 661 Research & Development
## 206 1376 31 Travel_Rarely 140 Research & Development
## 207 1377 28 Travel_Rarely 304 Sales
## 208 1378 33 Non-Travel 1283 Sales
## 209 1379 38 Travel_Rarely 437 Sales
## 210 1380 50 Travel_Rarely 854 Sales
## 211 1381 26 Travel_Frequently 575 Research & Development
## 212 1382 45 Travel_Rarely 549 Research & Development
## 213 1383 38 Travel_Frequently 240 Research & Development
## 214 1384 32 Travel_Rarely 638 Research & Development
## 215 1385 42 Travel_Rarely 201 Research & Development
## 216 1386 34 Travel_Rarely 1440 Sales
## 217 1387 29 Travel_Rarely 598 Research & Development
## 218 1388 31 Travel_Rarely 330 Research & Development
## 219 1389 23 Travel_Rarely 1309 Research & Development
## 220 1390 36 Non-Travel 217 Research & Development
## 221 1391 30 Travel_Rarely 570 Sales
## 222 1392 29 Travel_Rarely 1090 Sales
## 223 1393 42 Travel_Rarely 532 Research & Development
## 224 1394 51 Travel_Rarely 684 Research & Development
## 225 1395 22 Travel_Rarely 1230 Research & Development
## 226 1396 35 Travel_Rarely 1276 Research & Development
## 227 1397 35 Travel_Frequently 482 Research & Development
## 228 1398 35 Travel_Frequently 1199 Research & Development
## 229 1399 40 Travel_Rarely 1124 Sales
## 230 1400 37 Travel_Rarely 1107 Research & Development
## 231 1401 35 Travel_Rarely 104 Research & Development
## 232 1402 25 Travel_Rarely 141 Sales
## 233 1403 44 Travel_Rarely 1199 Research & Development
## 234 1404 35 Travel_Rarely 144 Research & Development
## 235 1405 31 Travel_Frequently 754 Sales
## 236 1406 35 Travel_Rarely 1258 Research & Development
## 237 1407 33 Travel_Rarely 924 Research & Development
## 238 1408 29 Travel_Rarely 441 Research & Development
## 239 1409 55 Travel_Rarely 452 Research & Development
## 240 1410 55 Travel_Rarely 1117 Sales
## 241 1411 22 Travel_Rarely 581 Research & Development
## 242 1412 32 Non-Travel 953 Research & Development
## 243 1413 50 Travel_Rarely 1464 Research & Development
## 244 1414 36 Travel_Rarely 1174 Sales
## 245 1415 40 Travel_Frequently 1395 Research & Development
## 246 1416 33 Travel_Frequently 827 Research & Development
## 247 1417 31 Travel_Frequently 523 Research & Development
## 248 1418 26 Travel_Rarely 991 Research & Development
## 249 1419 31 Travel_Frequently 1445 Research & Development
## 250 1420 35 Travel_Rarely 735 Research & Development
## 251 1421 33 Travel_Rarely 1017 Research & Development
## 252 1422 41 Non-Travel 247 Research & Development
## 253 1423 41 Travel_Rarely 427 Human Resources
## 254 1424 27 Non-Travel 443 Research & Development
## 255 1425 46 Travel_Rarely 406 Sales
## 256 1426 50 Travel_Rarely 1046 Research & Development
## 257 1427 45 Travel_Frequently 1199 Research & Development
## 258 1428 30 Non-Travel 879 Research & Development
## 259 1429 26 Travel_Rarely 950 Sales
## 260 1430 40 Travel_Rarely 1137 Research & Development
## 261 1431 55 Travel_Rarely 1136 Research & Development
## 262 1432 31 Non-Travel 587 Sales
## 263 1433 33 Non-Travel 722 Sales
## 264 1434 50 Travel_Frequently 878 Sales
## 265 1435 24 Non-Travel 673 Research & Development
## 266 1436 45 Travel_Rarely 950 Research & Development
## 267 1437 53 Travel_Rarely 1395 Research & Development
## 268 1438 31 Travel_Rarely 525 Sales
## 269 1439 39 Non-Travel 592 Research & Development
## 270 1440 35 Travel_Rarely 583 Research & Development
## 271 1441 38 Travel_Rarely 723 Sales
## 272 1442 23 Travel_Rarely 1243 Research & Development
## 273 1443 31 Non-Travel 325 Research & Development
## 274 1444 21 Travel_Rarely 156 Sales
## 275 1445 19 Travel_Rarely 489 Human Resources
## 276 1446 29 Travel_Rarely 592 Research & Development
## 277 1447 26 Travel_Frequently 1283 Sales
## 278 1448 43 Travel_Rarely 574 Research & Development
## 279 1449 32 Travel_Rarely 371 Sales
## 280 1450 22 Travel_Rarely 534 Research & Development
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## 283 1453 54 Travel_Rarely 1082 Sales
## 284 1454 56 Travel_Frequently 1240 Research & Development
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## 286 1456 23 Travel_Rarely 1320 Research & Development
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## 292 1462 37 Travel_Rarely 571 Research & Development
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## 294 1464 49 Travel_Frequently 1023 Sales
## 295 1465 26 Travel_Rarely 474 Research & Development
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## 297 1467 32 Travel_Rarely 1093 Sales
## 298 1468 26 Travel_Rarely 1357 Research & Development
## 299 1469 45 Travel_Rarely 252 Research & Development
## 300 1470 47 Travel_Rarely 249 Sales
## DistanceFromHome Education EducationField EmployeeCount
## 1 28 3 Life Sciences 1
## 2 2 3 Human Resources 1
## 3 21 3 Medical 1
## 4 2 3 Life Sciences 1
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## 1 Married 3407 25348 1 Y
## 2 Married 3600 8429 1 Y
## 3 Divorced 2377 19373 1 Y
## 4 Single 16659 23258 2 Y
## 5 Divorced 8620 23757 1 Y
## 6 Single 7484 25796 3 Y
## 7 Divorced 3482 19788 2 Y
## 8 Married 5377 3835 2 Y
## 9 Single 6812 17198 1 Y
## 10 Married 9824 22908 3 Y
## 11 Divorced 3617 25063 8 Y
## 12 Single 2501 18775 1 Y
## 13 Divorced 3055 6194 5 Y
## 14 Married 17639 6881 5 Y
## 15 Single 6388 22049 2 Y
## 16 Single 4332 14811 1 Y
## 17 Single 4320 24152 1 Y
## 18 Divorced 16124 3423 3 Y
## 19 Single 5160 21519 4 Y
## 20 Married 7654 5860 1 Y
## 21 Single 2837 15919 1 Y
## 22 Divorced 17399 6615 9 Y
## 23 Single 4440 25198 6 Y
## 24 Married 2145 2097 0 Y
## 25 Divorced 10851 19863 2 Y
## 26 Single 9980 15318 1 Y
## 27 Married 6430 20794 6 Y
## 28 Single 3447 24444 1 Y
## 29 Married 5507 16822 2 Y
## 30 Single 2422 25725 0 Y
## 31 Single 4930 13970 0 Y
## 32 Single 5562 9697 6 Y
## 33 Single 7880 2560 0 Y
## 34 Married 2705 9696 0 Y
## 35 Single 1416 17258 1 Y
## 36 Married 5301 2939 8 Y
## 37 Married 13525 14864 5 Y
## 38 Single 5399 14511 4 Y
## 39 Married 19272 21141 1 Y
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## 41 Single 4317 2302 3 Y
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## 46 Married 19161 13738 3 Y
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## 72 Single 4541 7744 1 Y
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## 75 Married 5562 21782 4 Y
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## 78 Single 4968 26427 0 Y
## 79 Married 3692 9256 1 Y
## 80 Married 2143 25527 4 Y
## 81 Married 5429 17491 4 Y
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## 89 Single 10965 12066 8 Y
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## 92 Married 6472 8989 1 Y
## 93 Single 3445 6152 1 Y
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## 101 Single 13591 14674 3 Y
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## 104 Married 17584 21016 3 Y
## 105 Married 2033 14470 1 Y
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## 115 Married 17875 11761 4 Y
## 116 Married 6804 23683 3 Y
## 117 Married 3579 9369 0 Y
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## 120 Single 13872 24409 0 Y
## 121 Married 4440 7636 0 Y
## 122 Married 7969 19609 2 Y
## 123 Married 19331 19519 4 Y
## 124 Married 2571 12290 4 Y
## 125 Married 4639 2261 2 Y
## 126 Married 11836 22789 5 Y
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## 143 Single 7945 19948 6 Y
## 144 Single 3038 12430 3 Y
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## 146 Single 9355 9558 1 Y
## 147 Single 4272 9558 4 Y
## 148 Single 1359 16154 1 Y
## 149 Single 3310 4488 1 Y
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## 169 Married 3221 3297 1 Y
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## 171 Single 3424 21632 7 Y
## 172 Married 2362 14669 4 Y
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## 179 Married 19513 9358 4 Y
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## 186 Single 8381 7507 7 Y
## 187 Single 16704 17119 1 Y
## 188 Single 3033 12828 1 Y
## 189 Divorced 10502 9659 7 Y
## 190 Single 4534 13352 0 Y
## 191 Divorced 5209 19760 1 Y
## 192 Divorced 3069 10302 0 Y
## 193 Married 5208 26312 1 Y
## 194 Married 2821 2997 3 Y
## 195 Married 2096 26376 1 Y
## 196 Single 3983 7621 0 Y
## 197 Divorced 9950 11533 9 Y
## 198 Married 4345 4381 0 Y
## 199 Divorced 14026 17588 1 Y
## 200 Married 2897 22474 3 Y
## 201 Divorced 19419 3735 2 Y
## 202 Married 2044 5531 1 Y
## 203 Divorced 2232 15417 7 Y
## 204 Divorced 10252 4235 2 Y
## 205 Married 12965 22308 4 Y
## 206 Married 3929 6984 8 Y
## 207 Single 5253 20750 1 Y
## 208 Single 5147 10697 8 Y
## 209 Single 4198 16379 2 Y
## 210 Divorced 19517 24118 3 Y
## 211 Single 3102 6582 0 Y
## 212 Married 3697 9278 9 Y
## 213 Single 5980 26085 6 Y
## 214 Married 5175 22162 5 Y
## 215 Divorced 2576 20490 3 Y
## 216 Married 2308 4944 0 Y
## 217 Married 2451 22376 6 Y
## 218 Married 6179 21057 1 Y
## 219 Divorced 2904 16092 1 Y
## 220 Single 7779 23238 2 Y
## 221 Divorced 6118 5431 1 Y
## 222 Divorced 2297 17967 1 Y
## 223 Married 19232 4933 1 Y
## 224 Single 19537 6462 7 Y
## 225 Married 4775 19146 6 Y
## 226 Married 7632 14295 4 Y
## 227 Single 4249 2690 1 Y
## 228 Married 5689 24594 1 Y
## 229 Married 7457 13273 2 Y
## 230 Divorced 3034 26914 1 Y
## 231 Divorced 2074 26619 1 Y
## 232 Married 4194 14363 1 Y
## 233 Divorced 19190 17477 1 Y
## 234 Single 4230 19225 0 Y
## 235 Married 5617 21075 1 Y
## 236 Single 2506 13301 3 Y
## 237 Single 2496 6670 4 Y
## 238 Married 9715 7288 3 Y
## 239 Single 19045 18938 0 Y
## 240 Single 16835 9873 3 Y
## 241 Single 3375 17624 0 Y
## 242 Single 2718 17674 2 Y
## 243 Married 19237 12853 2 Y
## 244 Single 9278 20763 3 Y
## 245 Divorced 5605 8504 1 Y
## 246 Married 5488 20161 1 Y
## 247 Married 3722 21081 6 Y
## 248 Married 2659 17759 1 Y
## 249 Single 7446 8931 1 Y
## 250 Married 3506 6020 0 Y
## 251 Single 2313 2993 4 Y
## 252 Divorced 19973 20284 1 Y
## 253 Divorced 19141 8861 3 Y
## 254 Married 1706 16571 1 Y
## 255 Married 17465 15596 3 Y
## 256 Single 10496 2755 6 Y
## 257 Married 6434 5118 4 Y
## 258 Single 4695 12858 7 Y
## 259 Single 5828 8450 1 Y
## 260 Divorced 16823 18991 2 Y
## 261 Divorced 14732 12414 2 Y
## 262 Divorced 9852 8935 1 Y
## 263 Single 17444 20489 1 Y
## 264 Divorced 6728 14255 7 Y
## 265 Divorced 4011 8232 0 Y
## 266 Married 2132 4585 4 Y
## 267 Married 7005 3458 3 Y
## 268 Divorced 5460 6219 4 Y
## 269 Single 3646 17181 2 Y
## 270 Divorced 10388 6975 1 Y
## 271 Married 5405 4244 2 Y
## 272 Married 1601 3445 1 Y
## 273 Single 9936 3787 0 Y
## 274 Single 2716 25422 1 Y
## 275 Single 2564 18437 1 Y
## 276 Single 2062 19384 3 Y
## 277 Single 4294 11148 1 Y
## 278 Married 7510 16873 1 Y
## 279 Married 9610 3840 3 Y
## 280 Single 2871 23785 1 Y
## 281 Divorced 2367 18779 5 Y
## 282 Divorced 2720 18959 1 Y
## 283 Married 10686 8392 6 Y
## 284 Married 2942 12154 2 Y
## 285 Single 5765 17485 5 Y
## 286 Single 3989 20586 1 Y
## 287 Divorced 2778 17725 4 Y
## 288 Single 2478 20938 1 Y
## 289 Divorced 3161 19920 3 Y
## 290 Married 2773 12145 0 Y
## 291 Single 5933 5197 9 Y
## 292 Divorced 2782 19905 0 Y
## 293 Married 2141 5348 1 Y
## 294 Married 5390 13243 2 Y
## 295 Married 2061 11133 1 Y
## 296 Single 2519 12287 4 Y
## 297 Single 5010 24301 1 Y
## 298 Single 2293 10558 1 Y
## 299 Married 19202 15970 0 Y
## 300 Married 4537 17783 0 Y
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 No 17 3 4
## 2 No 13 3 4
## 3 No 20 4 3
## 4 Yes 13 3 3
## 5 No 14 3 3
## 6 No 20 4 3
## 7 No 15 3 2
## 8 No 13 3 4
## 9 No 19 3 2
## 10 No 12 3 1
## 11 Yes 14 3 4
## 12 No 17 3 2
## 13 No 15 3 4
## 14 No 16 3 4
## 15 Yes 17 3 1
## 16 No 12 3 4
## 17 No 13 3 4
## 18 No 14 3 2
## 19 No 16 3 3
## 20 No 18 3 1
## 21 No 13 3 3
## 22 No 22 4 3
## 23 Yes 19 3 4
## 24 No 14 3 4
## 25 Yes 18 3 2
## 26 No 14 3 4
## 27 No 19 3 2
## 28 No 11 3 3
## 29 No 16 3 3
## 30 No 17 3 1
## 31 Yes 14 3 3
## 32 No 14 3 4
## 33 No 18 3 4
## 34 No 16 3 2
## 35 No 13 3 1
## 36 No 15 3 3
## 37 No 14 3 4
## 38 No 12 3 3
## 39 No 15 3 1
## 40 No 12 3 3
## 41 Yes 20 4 2
## 42 No 18 3 3
## 43 Yes 14 3 3
## 44 No 12 3 4
## 45 Yes 20 4 2
## 46 No 15 3 4
## 47 No 13 3 4
## 48 No 14 3 1
## 49 No 14 3 1
## 50 No 13 3 3
## 51 No 11 3 1
## 52 Yes 11 3 3
## 53 No 12 3 2
## 54 No 11 3 3
## 55 No 14 3 3
## 56 Yes 14 3 3
## 57 Yes 13 3 3
## 58 No 15 3 3
## 59 No 11 3 1
## 60 Yes 24 4 3
## 61 No 14 3 4
## 62 No 14 3 4
## 63 Yes 15 3 1
## 64 Yes 12 3 2
## 65 No 25 4 2
## 66 No 15 3 3
## 67 No 18 3 3
## 68 No 13 3 1
## 69 No 13 3 3
## 70 Yes 16 3 4
## 71 No 19 3 3
## 72 No 25 4 2
## 73 Yes 11 3 2
## 74 Yes 22 4 3
## 75 No 13 3 2
## 76 No 11 3 2
## 77 No 11 3 3
## 78 No 16 3 4
## 79 No 12 3 3
## 80 No 13 3 2
## 81 No 13 3 1
## 82 No 11 3 4
## 83 No 16 3 1
## 84 No 11 3 4
## 85 Yes 19 3 4
## 86 Yes 11 3 2
## 87 No 11 3 3
## 88 Yes 18 3 4
## 89 No 24 4 3
## 90 No 17 3 3
## 91 Yes 19 3 2
## 92 Yes 15 3 4
## 93 No 11 3 3
## 94 Yes 19 3 1
## 95 No 15 3 3
## 96 No 18 3 4
## 97 No 20 4 3
## 98 No 25 4 4
## 99 No 14 3 4
## 100 No 12 3 3
## 101 Yes 18 3 3
## 102 No 22 4 3
## 103 Yes 15 3 2
## 104 Yes 16 3 4
## 105 No 18 3 3
## 106 No 12 3 2
## 107 Yes 13 3 4
## 108 Yes 11 3 4
## 109 Yes 22 4 4
## 110 Yes 12 3 3
## 111 Yes 18 3 3
## 112 No 13 3 4
## 113 No 13 3 3
## 114 No 18 3 1
## 115 Yes 13 3 3
## 116 No 18 3 3
## 117 Yes 21 4 1
## 118 No 22 4 4
## 119 No 11 3 3
## 120 No 13 3 3
## 121 No 15 3 1
## 122 Yes 14 3 3
## 123 Yes 16 3 3
## 124 No 17 3 3
## 125 No 16 3 4
## 126 No 14 3 3
## 127 No 11 3 3
## 128 No 15 3 2
## 129 No 11 3 3
## 130 No 15 3 3
## 131 No 11 3 4
## 132 No 13 3 3
## 133 Yes 12 3 4
## 134 Yes 12 3 3
## 135 No 14 3 4
## 136 No 18 3 2
## 137 Yes 17 3 1
## 138 No 14 3 2
## 139 No 19 3 4
## 140 No 15 3 3
## 141 No 18 3 3
## 142 No 13 3 2
## 143 Yes 15 3 4
## 144 No 20 4 1
## 145 No 12 3 1
## 146 No 18 3 3
## 147 No 19 3 1
## 148 No 12 3 2
## 149 No 21 4 4
## 150 No 21 4 3
## 151 No 13 3 3
## 152 No 17 3 4
## 153 No 22 4 4
## 154 No 14 3 2
## 155 Yes 20 4 4
## 156 No 12 3 3
## 157 No 20 4 3
## 158 Yes 12 3 3
## 159 No 14 3 4
## 160 Yes 13 3 3
## 161 Yes 16 3 2
## 162 No 14 3 2
## 163 No 11 3 3
## 164 Yes 13 3 1
## 165 Yes 13 3 2
## 166 No 16 3 4
## 167 Yes 24 4 1
## 168 Yes 13 3 4
## 169 Yes 11 3 3
## 170 No 16 3 4
## 171 No 13 3 3
## 172 No 12 3 3
## 173 No 19 3 1
## 174 No 12 3 3
## 175 No 11 3 2
## 176 No 23 4 4
## 177 No 16 3 2
## 178 No 18 3 4
## 179 Yes 12 3 1
## 180 Yes 18 3 3
## 181 No 14 3 1
## 182 No 12 3 4
## 183 No 15 3 4
## 184 No 11 3 3
## 185 No 11 3 3
## 186 No 20 4 4
## 187 No 11 3 3
## 188 No 12 3 1
## 189 No 17 3 1
## 190 No 11 3 1
## 191 Yes 12 3 2
## 192 No 15 3 4
## 193 No 11 3 4
## 194 No 16 3 1
## 195 No 11 3 3
## 196 No 17 3 3
## 197 Yes 15 3 3
## 198 No 12 3 4
## 199 Yes 11 3 2
## 200 No 11 3 3
## 201 No 17 3 2
## 202 No 11 3 3
## 203 No 14 3 3
## 204 Yes 21 4 3
## 205 Yes 20 4 4
## 206 Yes 23 4 3
## 207 No 16 3 4
## 208 No 15 3 4
## 209 No 12 3 2
## 210 No 11 3 3
## 211 No 22 4 3
## 212 No 14 3 1
## 213 Yes 12 3 4
## 214 No 12 3 3
## 215 No 16 3 2
## 216 Yes 25 4 2
## 217 No 18 3 1
## 218 Yes 15 3 4
## 219 No 12 3 3
## 220 No 20 4 1
## 221 No 13 3 3
## 222 No 14 3 4
## 223 No 11 3 4
## 224 No 13 3 3
## 225 No 22 4 1
## 226 Yes 12 3 3
## 227 Yes 11 3 2
## 228 Yes 14 3 4
## 229 Yes 22 4 3
## 230 No 12 3 3
## 231 Yes 12 3 4
## 232 Yes 18 3 4
## 233 No 14 3 4
## 234 No 15 3 3
## 235 Yes 11 3 3
## 236 No 13 3 3
## 237 No 11 3 4
## 238 No 13 3 3
## 239 Yes 14 3 3
## 240 No 23 4 4
## 241 No 12 3 4
## 242 No 14 3 2
## 243 Yes 11 3 4
## 244 Yes 16 3 4
## 245 No 11 3 1
## 246 Yes 13 3 1
## 247 Yes 13 3 3
## 248 Yes 13 3 3
## 249 No 11 3 1
## 250 Yes 14 3 4
## 251 Yes 20 4 2
## 252 No 22 4 2
## 253 No 15 3 2
## 254 No 11 3 3
## 255 No 12 3 4
## 256 No 15 3 4
## 257 No 17 3 4
## 258 Yes 18 3 3
## 259 Yes 12 3 2
## 260 No 11 3 1
## 261 No 13 3 4
## 262 Yes 19 3 1
## 263 No 11 3 4
## 264 No 12 3 4
## 265 No 18 3 4
## 266 No 20 4 4
## 267 No 15 3 3
## 268 No 22 4 4
## 269 Yes 23 4 2
## 270 Yes 11 3 3
## 271 Yes 20 4 1
## 272 Yes 21 4 3
## 273 No 19 3 2
## 274 No 15 3 4
## 275 No 12 3 3
## 276 No 14 3 2
## 277 No 12 3 2
## 278 No 17 3 2
## 279 No 13 3 3
## 280 No 15 3 3
## 281 No 12 3 1
## 282 No 18 3 4
## 283 No 11 3 2
## 284 No 19 3 2
## 285 No 11 3 1
## 286 Yes 11 3 1
## 287 Yes 13 3 3
## 288 Yes 12 3 2
## 289 Yes 22 4 4
## 290 No 20 4 4
## 291 No 12 3 4
## 292 Yes 13 3 2
## 293 No 12 3 2
## 294 No 14 3 4
## 295 No 21 4 1
## 296 No 21 4 3
## 297 No 16 3 1
## 298 No 12 3 3
## 299 No 11 3 3
## 300 Yes 22 4 1
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 1 80 2 10 3
## 2 80 1 5 2
## 3 80 1 1 0
## 4 80 0 30 2
## 5 80 2 10 3
## 6 80 0 23 1
## 7 80 2 16 3
## 8 80 3 10 3
## 9 80 0 10 2
## 10 80 0 12 2
## 11 80 1 3 2
## 12 80 0 1 4
## 13 80 2 11 2
## 14 80 0 30 3
## 15 80 0 14 6
## 16 80 0 20 2
## 17 80 0 5 2
## 18 80 2 9 2
## 19 80 0 12 3
## 20 80 2 9 3
## 21 80 0 6 3
## 22 80 1 32 1
## 23 80 0 9 3
## 24 80 1 3 2
## 25 80 1 24 2
## 26 80 0 10 3
## 27 80 1 10 4
## 28 80 0 3 2
## 29 80 2 12 1
## 30 80 0 4 3
## 31 80 0 6 2
## 32 80 0 19 3
## 33 80 0 9 3
## 34 80 1 6 2
## 35 80 0 1 6
## 36 80 2 4 2
## 37 80 1 23 2
## 38 80 0 12 3
## 39 80 1 21 2
## 40 80 0 25 3
## 41 80 0 19 2
## 42 80 0 3 2
## 43 80 3 21 2
## 44 80 0 25 6
## 45 80 1 9 3
## 46 80 0 28 3
## 47 80 0 31 2
## 48 80 2 5 2
## 49 80 0 7 2
## 50 80 2 5 5
## 51 80 0 34 3
## 52 80 0 8 3
## 53 80 1 4 2
## 54 80 1 18 2
## 55 80 1 17 1
## 56 80 1 21 3
## 57 80 0 10 2
## 58 80 1 9 3
## 59 80 1 12 2
## 60 80 1 10 2
## 61 80 0 21 2
## 62 80 1 6 2
## 63 80 0 1 2
## 64 80 0 22 2
## 65 80 0 10 5
## 66 80 0 6 3
## 67 80 0 33 0
## 68 80 0 8 2
## 69 80 2 3 3
## 70 80 0 18 2
## 71 80 0 10 5
## 72 80 0 20 3
## 73 80 2 9 4
## 74 80 1 16 2
## 75 80 1 12 2
## 76 80 0 5 2
## 77 80 0 15 2
## 78 80 0 10 2
## 79 80 0 12 2
## 80 80 1 8 2
## 81 80 2 10 1
## 82 80 1 16 2
## 83 80 2 17 2
## 84 80 2 14 5
## 85 80 1 12 4
## 86 80 1 24 3
## 87 80 0 13 4
## 88 80 0 33 2
## 89 80 0 26 2
## 90 80 0 6 2
## 91 80 1 9 2
## 92 80 1 9 2
## 93 80 0 6 5
## 94 80 0 28 1
## 95 80 1 5 2
## 96 80 1 7 6
## 97 80 1 19 2
## 98 80 1 10 3
## 99 80 0 7 1
## 100 80 0 1 3
## 101 80 0 16 3
## 102 80 0 12 2
## 103 80 1 7 3
## 104 80 3 21 5
## 105 80 1 1 2
## 106 80 0 8 2
## 107 80 1 9 3
## 108 80 1 8 2
## 109 80 0 7 3
## 110 80 0 23 3
## 111 80 1 5 0
## 112 80 0 17 5
## 113 80 2 9 2
## 114 80 1 9 3
## 115 80 1 29 2
## 116 80 1 7 5
## 117 80 1 12 2
## 118 80 3 10 3
## 119 80 3 1 4
## 120 80 0 38 1
## 121 80 2 16 3
## 122 80 0 28 4
## 123 80 1 27 2
## 124 80 1 17 3
## 125 80 1 17 2
## 126 80 1 28 3
## 127 80 0 1 6
## 128 80 1 28 2
## 129 80 1 24 4
## 130 80 0 4 6
## 131 80 1 14 4
## 132 80 1 24 1
## 133 80 0 25 2
## 134 80 0 7 3
## 135 80 1 24 4
## 136 80 1 10 6
## 137 80 1 17 2
## 138 80 1 4 3
## 139 80 3 3 4
## 140 80 0 6 3
## 141 80 1 20 2
## 142 80 1 24 4
## 143 80 0 18 2
## 144 80 0 8 2
## 145 80 3 10 4
## 146 80 0 8 5
## 147 80 0 16 3
## 148 80 0 1 3
## 149 80 0 5 3
## 150 80 1 36 3
## 151 80 1 2 2
## 152 80 0 19 4
## 153 80 1 22 3
## 154 80 0 9 3
## 155 80 0 5 2
## 156 80 1 4 3
## 157 80 3 5 2
## 158 80 0 9 2
## 159 80 0 0 6
## 160 80 1 10 3
## 161 80 1 18 3
## 162 80 2 8 2
## 163 80 0 8 2
## 164 80 2 10 2
## 165 80 1 17 1
## 166 80 0 6 4
## 167 80 1 31 5
## 168 80 1 6 0
## 169 80 3 20 3
## 170 80 0 5 3
## 171 80 0 6 3
## 172 80 0 10 4
## 173 80 0 9 2
## 174 80 0 2 3
## 175 80 1 5 2
## 176 80 1 10 3
## 177 80 1 7 2
## 178 80 1 1 5
## 179 80 1 26 2
## 180 80 1 23 2
## 181 80 1 5 2
## 182 80 2 6 3
## 183 80 1 5 2
## 184 80 1 14 5
## 185 80 1 25 3
## 186 80 0 18 2
## 187 80 0 21 2
## 188 80 0 2 2
## 189 80 1 33 2
## 190 80 0 9 6
## 191 80 3 11 4
## 192 80 1 11 3
## 193 80 0 16 2
## 194 80 1 8 2
## 195 80 0 7 1
## 196 80 0 4 2
## 197 80 3 11 2
## 198 80 1 6 2
## 199 80 1 33 2
## 200 80 2 9 6
## 201 80 1 21 2
## 202 80 1 5 6
## 203 80 3 7 1
## 204 80 1 17 2
## 205 80 3 27 2
## 206 80 1 7 0
## 207 80 0 7 1
## 208 80 0 13 2
## 209 80 0 8 5
## 210 80 1 32 3
## 211 80 0 7 2
## 212 80 2 12 3
## 213 80 0 17 2
## 214 80 1 9 3
## 215 80 1 8 5
## 216 80 1 12 4
## 217 80 2 5 2
## 218 80 2 10 3
## 219 80 2 4 2
## 220 80 0 18 0
## 221 80 3 10 2
## 222 80 2 2 2
## 223 80 0 22 3
## 224 80 0 23 5
## 225 80 2 4 2
## 226 80 0 10 2
## 227 80 0 9 3
## 228 80 2 10 2
## 229 80 3 6 2
## 230 80 1 18 2
## 231 80 1 1 2
## 232 80 0 5 3
## 233 80 2 26 4
## 234 80 0 6 2
## 235 80 0 10 4
## 236 80 0 7 0
## 237 80 0 7 3
## 238 80 1 9 3
## 239 80 0 37 2
## 240 80 0 37 2
## 241 80 0 4 2
## 242 80 0 12 3
## 243 80 1 29 2
## 244 80 0 15 3
## 245 80 1 20 2
## 246 80 1 6 2
## 247 80 1 7 2
## 248 80 1 3 2
## 249 80 0 10 2
## 250 80 0 4 3
## 251 80 0 5 0
## 252 80 2 21 3
## 253 80 3 23 2
## 254 80 3 0 6
## 255 80 1 23 3
## 256 80 0 20 2
## 257 80 1 9 1
## 258 80 0 10 3
## 259 80 0 8 0
## 260 80 1 22 3
## 261 80 2 31 4
## 262 80 1 10 5
## 263 80 0 10 2
## 264 80 2 12 3
## 265 80 1 5 5
## 266 80 1 8 3
## 267 80 0 11 2
## 268 80 2 13 4
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## 270 80 1 16 3
## 271 80 2 20 4
## 272 80 2 1 2
## 273 80 0 10 2
## 274 80 0 1 0
## 275 80 0 1 3
## 276 80 0 11 2
## 277 80 0 7 2
## 278 80 1 10 1
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## 280 80 0 1 5
## 281 80 1 6 2
## 282 80 1 10 5
## 283 80 1 13 4
## 284 80 1 18 4
## 285 80 0 7 4
## 286 80 0 5 2
## 287 80 1 10 1
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## 289 80 1 19 0
## 290 80 0 3 3
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## 292 80 2 6 3
## 293 80 1 6 3
## 294 80 0 17 3
## 295 80 0 1 5
## 296 80 0 16 6
## 297 80 0 12 0
## 298 80 0 1 2
## 299 80 1 25 2
## 300 80 1 8 2
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 2 10 9
## 2 3 5 4
## 3 2 1 1
## 4 3 5 4
## 5 3 10 7
## 6 2 13 12
## 7 2 9 8
## 8 3 7 7
## 9 3 10 9
## 10 3 1 0
## 11 3 1 1
## 12 3 1 1
## 13 2 9 8
## 14 3 4 3
## 15 3 0 0
## 16 3 20 9
## 17 3 5 3
## 18 2 7 7
## 19 2 9 7
## 20 4 9 8
## 21 3 6 2
## 22 2 5 4
## 23 3 5 2
## 24 3 2 2
## 25 3 7 7
## 26 2 10 3
## 27 3 3 2
## 28 3 3 2
## 29 1 4 2
## 30 3 3 2
## 31 4 5 4
## 32 3 10 7
## 33 3 8 7
## 34 4 5 4
## 35 2 1 0
## 36 2 2 1
## 37 4 20 4
## 38 3 4 2
## 39 3 21 9
## 40 4 1 0
## 41 3 3 2
## 42 3 2 2
## 43 3 5 0
## 44 2 9 7
## 45 2 1 0
## 46 3 5 4
## 47 1 5 2
## 48 3 5 2
## 49 1 6 5
## 50 3 5 3
## 51 4 34 6
## 52 3 8 7
## 53 3 3 0
## 54 2 7 7
## 55 4 15 11
## 56 3 21 8
## 57 2 10 9
## 58 1 5 3
## 59 3 7 7
## 60 2 3 2
## 61 3 20 15
## 62 4 5 2
## 63 3 1 0
## 64 2 21 9
## 65 4 5 2
## 66 3 3 2
## 67 3 12 9
## 68 4 5 4
## 69 3 2 2
## 70 3 1 0
## 71 3 1 1
## 72 3 20 11
## 73 3 5 3
## 74 3 5 3
## 75 2 5 2
## 76 2 3 2
## 77 3 3 2
## 78 3 9 7
## 79 2 11 10
## 80 3 5 2
## 81 3 8 7
## 82 2 15 1
## 83 2 1 0
## 84 4 10 9
## 85 3 10 7
## 86 2 5 3
## 87 3 12 6
## 88 4 33 7
## 89 3 5 2
## 90 2 4 3
## 91 3 8 7
## 92 3 9 8
## 93 2 6 2
## 94 2 8 3
## 95 3 5 4
## 96 3 2 1
## 97 4 1 0
## 98 3 10 0
## 99 2 6 2
## 100 4 1 0
## 101 3 1 0
## 102 3 11 8
## 103 4 7 7
## 104 2 5 3
## 105 4 1 0
## 106 3 8 7
## 107 3 3 2
## 108 3 5 2
## 109 3 3 2
## 110 3 5 3
## 111 2 4 2
## 112 3 16 6
## 113 3 6 2
## 114 1 7 7
## 115 2 1 0
## 116 3 2 2
## 117 3 11 9
## 118 2 10 4
## 119 3 1 0
## 120 2 37 10
## 121 3 15 13
## 122 3 5 4
## 123 3 1 0
## 124 3 5 2
## 125 2 15 7
## 126 3 2 0
## 127 3 1 0
## 128 3 27 10
## 129 2 4 2
## 130 3 3 2
## 131 1 10 9
## 132 4 20 6
## 133 3 7 1
## 134 2 2 2
## 135 3 1 0
## 136 2 7 7
## 137 2 16 8
## 138 3 1 0
## 139 3 3 2
## 140 3 2 2
## 141 1 17 9
## 142 2 24 7
## 143 2 4 2
## 144 3 5 4
## 145 3 10 8
## 146 3 8 7
## 147 3 1 0
## 148 3 1 0
## 149 3 5 3
## 150 3 36 6
## 151 2 2 2
## 152 3 2 2
## 153 2 22 3
## 154 3 7 7
## 155 2 4 2
## 156 3 4 2
## 157 3 5 4
## 158 3 5 4
## 159 3 0 0
## 160 3 4 0
## 161 3 1 0
## 162 2 8 6
## 163 3 4 2
## 164 3 7 7
## 165 2 5 3
## 166 3 3 2
## 167 2 29 10
## 168 3 5 4
## 169 3 20 8
## 170 3 5 4
## 171 2 4 3
## 172 4 3 2
## 173 1 7 6
## 174 3 2 2
## 175 3 5 2
## 176 3 10 7
## 177 3 4 2
## 178 2 1 0
## 179 4 2 2
## 180 3 12 11
## 181 3 2 2
## 182 2 5 3
## 183 2 3 2
## 184 3 5 4
## 185 3 25 10
## 186 4 14 7
## 187 3 21 6
## 188 2 2 2
## 189 1 5 4
## 190 3 8 7
## 191 2 11 8
## 192 3 10 8
## 193 3 16 15
## 194 3 2 2
## 195 3 7 4
## 196 3 3 2
## 197 3 3 2
## 198 3 5 4
## 199 3 33 9
## 200 2 4 3
## 201 4 18 16
## 202 4 5 3
## 203 3 3 2
## 204 3 7 7
## 205 2 3 2
## 206 3 4 2
## 207 3 7 5
## 208 2 11 7
## 209 4 3 2
## 210 2 7 0
## 211 3 6 4
## 212 3 10 9
## 213 3 15 7
## 214 2 5 3
## 215 3 5 2
## 216 3 11 10
## 217 2 1 0
## 218 2 10 2
## 219 2 4 2
## 220 3 11 9
## 221 3 10 9
## 222 3 2 2
## 223 3 22 17
## 224 3 20 18
## 225 1 2 2
## 226 3 8 7
## 227 3 9 6
## 228 4 10 2
## 229 2 4 3
## 230 2 18 7
## 231 3 1 0
## 232 3 5 3
## 233 2 25 9
## 234 3 5 4
## 235 3 10 7
## 236 3 2 2
## 237 3 1 1
## 238 3 7 7
## 239 3 36 10
## 240 3 10 9
## 241 4 3 2
## 242 3 7 7
## 243 2 8 1
## 244 3 5 4
## 245 3 20 7
## 246 3 6 5
## 247 1 2 2
## 248 3 3 2
## 249 3 10 8
## 250 3 3 2
## 251 3 2 2
## 252 3 21 16
## 253 2 21 6
## 254 2 0 0
## 255 3 12 9
## 256 3 4 3
## 257 3 3 2
## 258 3 8 4
## 259 3 8 7
## 260 3 19 7
## 261 4 7 7
## 262 2 10 8
## 263 3 10 8
## 264 3 6 3
## 265 2 4 2
## 266 3 5 4
## 267 3 4 3
## 268 4 7 7
## 269 4 1 0
## 270 2 16 10
## 271 2 4 2
## 272 3 0 0
## 273 3 9 4
## 274 3 1 0
## 275 4 1 0
## 276 3 3 2
## 277 3 7 7
## 278 3 10 9
## 279 1 4 3
## 280 3 0 0
## 281 2 4 1
## 282 3 10 7
## 283 3 9 4
## 284 3 5 4
## 285 1 5 3
## 286 3 5 4
## 287 2 7 7
## 288 2 4 3
## 289 1 1 0
## 290 3 2 2
## 291 2 5 2
## 292 2 5 3
## 293 2 6 4
## 294 2 9 6
## 295 3 1 0
## 296 3 11 8
## 297 3 11 8
## 298 2 1 0
## 299 3 24 0
## 300 3 7 6
## YearsSinceLastPromotion YearsWithCurrManager
## 1 6 8
## 2 1 4
## 3 0 0
## 4 1 2
## 5 0 4
## 6 12 8
## 7 0 0
## 8 7 7
## 9 1 8
## 10 0 0
## 11 0 0
## 12 1 0
## 13 1 7
## 14 0 3
## 15 0 0
## 16 3 7
## 17 0 2
## 18 1 7
## 19 7 3
## 20 7 7
## 21 4 1
## 22 1 3
## 23 1 4
## 24 2 1
## 25 0 7
## 26 9 7
## 27 1 2
## 28 1 2
## 29 1 3
## 30 1 2
## 31 1 4
## 32 0 9
## 33 0 7
## 34 0 3
## 35 1 0
## 36 2 2
## 37 4 8
## 38 0 3
## 39 13 3
## 40 0 0
## 41 2 2
## 42 2 2
## 43 0 2
## 44 5 4
## 45 0 0
## 46 4 3
## 47 1 4
## 48 0 3
## 49 1 3
## 50 1 3
## 51 1 16
## 52 3 0
## 53 0 2
## 54 0 7
## 55 5 9
## 56 1 6
## 57 7 8
## 58 1 4
## 59 1 7
## 60 0 2
## 61 1 12
## 62 0 3
## 63 0 0
## 64 13 14
## 65 0 0
## 66 0 2
## 67 3 8
## 68 1 2
## 69 2 1
## 70 0 0
## 71 0 0
## 72 13 17
## 73 1 4
## 74 0 4
## 75 2 2
## 76 1 2
## 77 1 2
## 78 0 7
## 79 0 7
## 80 0 4
## 81 7 7
## 82 0 9
## 83 0 0
## 84 1 8
## 85 0 8
## 86 0 2
## 87 2 11
## 88 15 12
## 89 0 0
## 90 1 2
## 91 6 7
## 92 5 8
## 93 1 4
## 94 0 7
## 95 1 2
## 96 2 2
## 97 0 0
## 98 7 9
## 99 1 5
## 100 0 0
## 101 0 0
## 102 5 8
## 103 5 6
## 104 1 3
## 105 0 0
## 106 1 3
## 107 0 2
## 108 1 4
## 109 0 2
## 110 4 4
## 111 3 2
## 112 0 13
## 113 1 3
## 114 1 7
## 115 0 0
## 116 2 2
## 117 5 7
## 118 1 9
## 119 0 0
## 120 1 8
## 121 5 8
## 122 0 4
## 123 0 0
## 124 0 3
## 125 6 13
## 126 2 2
## 127 0 0
## 128 15 7
## 129 1 2
## 130 1 2
## 131 9 8
## 132 14 17
## 133 0 7
## 134 2 2
## 135 1 0
## 136 6 2
## 137 4 11
## 138 0 0
## 139 1 2
## 140 2 2
## 141 0 15
## 142 14 9
## 143 3 3
## 144 1 4
## 145 7 7
## 146 7 7
## 147 0 0
## 148 0 0
## 149 0 2
## 150 2 13
## 151 0 2
## 152 2 2
## 153 11 11
## 154 0 2
## 155 2 3
## 156 3 2
## 157 0 0
## 158 1 0
## 159 0 0
## 160 2 3
## 161 0 0
## 162 1 7
## 163 1 3
## 164 7 7
## 165 1 3
## 166 1 2
## 167 11 10
## 168 1 4
## 169 3 8
## 170 0 3
## 171 0 1
## 172 1 2
## 173 0 7
## 174 2 1
## 175 0 4
## 176 1 7
## 177 0 2
## 178 0 1
## 179 0 1
## 180 11 11
## 181 2 0
## 182 1 4
## 183 0 2
## 184 0 4
## 185 3 9
## 186 8 10
## 187 8 6
## 188 1 2
## 189 1 4
## 190 1 7
## 191 2 7
## 192 0 7
## 193 1 10
## 194 2 2
## 195 0 6
## 196 2 2
## 197 0 2
## 198 1 4
## 199 0 10
## 200 2 3
## 201 0 11
## 202 0 3
## 203 1 2
## 204 7 7
## 205 0 2
## 206 0 2
## 207 0 7
## 208 1 7
## 209 1 2
## 210 0 6
## 211 0 4
## 212 9 8
## 213 4 12
## 214 1 3
## 215 1 2
## 216 5 7
## 217 0 0
## 218 6 7
## 219 0 2
## 220 0 9
## 221 1 2
## 222 2 2
## 223 11 15
## 224 15 15
## 225 2 2
## 226 0 0
## 227 1 1
## 228 0 2
## 229 0 2
## 230 12 17
## 231 0 0
## 232 0 3
## 233 14 13
## 234 4 3
## 235 0 8
## 236 2 2
## 237 0 0
## 238 0 7
## 239 4 13
## 240 7 7
## 241 1 2
## 242 0 7
## 243 7 7
## 244 0 1
## 245 2 13
## 246 1 2
## 247 2 2
## 248 0 2
## 249 4 7
## 250 2 2
## 251 2 2
## 252 5 10
## 253 12 6
## 254 0 0
## 255 4 9
## 256 1 3
## 257 0 2
## 258 1 7
## 259 7 4
## 260 11 16
## 261 0 0
## 262 9 6
## 263 6 0
## 264 0 1
## 265 1 3
## 266 0 3
## 267 1 2
## 268 5 7
## 269 0 0
## 270 10 1
## 271 0 3
## 272 0 0
## 273 1 7
## 274 0 0
## 275 0 0
## 276 1 2
## 277 0 7
## 278 0 9
## 279 0 2
## 280 0 0
## 281 0 3
## 282 2 8
## 283 7 0
## 284 0 3
## 285 0 0
## 286 1 2
## 287 1 0
## 288 1 2
## 289 0 0
## 290 2 2
## 291 2 3
## 292 4 3
## 293 1 1
## 294 0 8
## 295 0 0
## 296 3 9
## 297 5 7
## 298 0 1
## 299 1 7
## 300 7 7#File for Linear Regression Predictions
Case2PredictionsSeanMcWhirter=read.csv("C:/Users/seans/Desktop/DDS/Unit 14 and 15 Case Study 2/CaseStudy2CompSetNoSalary.csv", header=TRUE, sep=",", stringsAsFactors = TRUE)
saveRDS(Case2PredictionsSeanMcWhirter, file ="Case2PredictionsSeanMcWhirter_Attrition.Rds")
readRDS(Case2PredictionsSeanMcWhirter, file ="Case2PredictionsSeanMcWhirter_Attrition.Rds")## ID Age Attrition BusinessTravel DailyRate Department
## 1 871 43 No Travel_Frequently 1422 Sales
## 2 872 33 No Travel_Rarely 461 Research & Development
## 3 873 55 Yes Travel_Rarely 267 Sales
## 4 874 36 No Non-Travel 1351 Research & Development
## 5 875 27 No Travel_Rarely 1302 Research & Development
## 6 876 39 Yes Travel_Rarely 895 Sales
## 7 877 33 No Non-Travel 750 Sales
## 8 878 21 Yes Travel_Frequently 251 Research & Development
## 9 879 30 No Travel_Frequently 1312 Research & Development
## 10 880 51 No Travel_Rarely 1405 Research & Development
## 11 881 46 Yes Travel_Rarely 377 Sales
## 12 882 40 No Travel_Rarely 329 Research & Development
## 13 883 29 No Travel_Rarely 1176 Sales
## 14 884 27 No Travel_Frequently 829 Sales
## 15 885 29 No Travel_Rarely 1210 Sales
## 16 886 22 Yes Travel_Rarely 617 Research & Development
## 17 887 29 No Travel_Rarely 726 Research & Development
## 18 888 34 No Travel_Rarely 167 Research & Development
## 19 889 31 No Travel_Rarely 655 Research & Development
## 20 890 35 No Travel_Rarely 528 Human Resources
## 21 891 40 No Travel_Rarely 523 Research & Development
## 22 892 30 No Travel_Rarely 1288 Sales
## 23 893 46 Yes Travel_Rarely 669 Sales
## 24 894 27 No Travel_Frequently 1410 Sales
## 25 895 49 No Travel_Rarely 1490 Research & Development
## 26 896 46 No Travel_Rarely 706 Research & Development
## 27 897 33 Yes Travel_Rarely 211 Sales
## 28 898 42 No Non-Travel 495 Research & Development
## 29 899 34 No Travel_Rarely 507 Sales
## 30 900 36 No Travel_Rarely 329 Research & Development
## 31 901 27 No Travel_Rarely 591 Research & Development
## 32 902 44 No Travel_Frequently 383 Sales
## 33 903 48 No Travel_Rarely 492 Sales
## 34 904 40 No Travel_Rarely 543 Research & Development
## 35 905 45 No Travel_Rarely 1234 Sales
## 36 906 33 No Travel_Frequently 970 Sales
## 37 907 46 No Travel_Frequently 638 Research & Development
## 38 908 37 No Travel_Rarely 1192 Research & Development
## 39 909 28 Yes Travel_Rarely 1434 Research & Development
## 40 910 32 No Travel_Rarely 827 Research & Development
## 41 911 43 No Travel_Rarely 920 Research & Development
## 42 912 38 No Travel_Frequently 1444 Human Resources
## 43 913 40 No Travel_Rarely 616 Research & Development
## 44 914 34 No Travel_Rarely 1354 Research & Development
## 45 915 41 No Travel_Rarely 549 Research & Development
## 46 916 23 No Travel_Rarely 507 Research & Development
## 47 917 28 No Travel_Rarely 1217 Research & Development
## 48 918 26 No Travel_Rarely 572 Sales
## 49 919 28 No Travel_Rarely 1144 Sales
## 50 920 59 No Travel_Rarely 1324 Research & Development
## 51 921 30 No Travel_Rarely 413 Sales
## 52 922 35 No Travel_Rarely 1229 Research & Development
## 53 923 36 Yes Travel_Rarely 660 Research & Development
## 54 924 42 No Non-Travel 179 Human Resources
## 55 925 54 No Travel_Rarely 584 Research & Development
## 56 926 40 No Travel_Rarely 1171 Research & Development
## 57 927 32 No Travel_Rarely 604 Sales
## 58 928 38 No Travel_Rarely 201 Research & Development
## 59 929 22 No Non-Travel 1123 Research & Development
## 60 930 32 No Travel_Frequently 1005 Research & Development
## 61 931 34 No Non-Travel 1375 Sales
## 62 932 32 No Travel_Rarely 1018 Research & Development
## 63 933 41 No Travel_Rarely 645 Sales
## 64 934 58 Yes Travel_Frequently 781 Research & Development
## 65 935 20 Yes Travel_Rarely 129 Research & Development
## 66 936 24 Yes Travel_Frequently 381 Research & Development
## 67 937 46 No Travel_Rarely 644 Research & Development
## 68 938 51 Yes Travel_Frequently 1150 Research & Development
## 69 939 39 No Travel_Rarely 116 Research & Development
## 70 940 28 No Travel_Rarely 1172 Sales
## 71 941 40 Yes Travel_Rarely 676 Research & Development
## 72 942 39 No Travel_Rarely 1462 Sales
## 73 943 59 No Travel_Rarely 1435 Sales
## 74 944 27 No Travel_Rarely 1055 Research & Development
## 75 945 18 No Travel_Rarely 812 Sales
## 76 946 29 No Travel_Frequently 995 Research & Development
## 77 947 21 Yes Travel_Rarely 337 Sales
## 78 948 26 No Travel_Rarely 1349 Research & Development
## 79 949 26 Yes Travel_Frequently 426 Human Resources
## 80 950 47 Yes Travel_Frequently 719 Sales
## 81 951 42 No Non-Travel 355 Research & Development
## 82 952 40 No Travel_Rarely 300 Sales
## 83 953 37 No Travel_Rarely 124 Research & Development
## 84 954 27 No Travel_Frequently 1297 Research & Development
## 85 955 39 No Non-Travel 439 Research & Development
## 86 956 45 No Travel_Rarely 176 Human Resources
## 87 957 56 No Travel_Rarely 1400 Research & Development
## 88 958 32 No Non-Travel 1109 Research & Development
## 89 959 41 No Travel_Rarely 1206 Sales
## 90 960 58 Yes Travel_Rarely 286 Research & Development
## 91 961 29 No Travel_Rarely 665 Research & Development
## 92 962 40 No Travel_Rarely 1300 Research & Development
## 93 963 38 No Travel_Frequently 471 Research & Development
## 94 964 39 No Travel_Rarely 141 Human Resources
## 95 965 25 No Travel_Rarely 1280 Research & Development
## 96 966 50 No Travel_Rarely 989 Research & Development
## 97 967 29 No Travel_Rarely 468 Research & Development
## 98 968 36 No Travel_Rarely 164 Sales
## 99 969 50 No Travel_Frequently 1421 Research & Development
## 100 970 26 Yes Travel_Rarely 1449 Research & Development
## 101 971 27 No Travel_Rarely 954 Sales
## 102 972 31 No Travel_Rarely 616 Research & Development
## 103 973 28 No Travel_Frequently 791 Research & Development
## 104 974 20 No Travel_Rarely 959 Research & Development
## 105 975 24 No Travel_Rarely 1206 Research & Development
## 106 976 29 Yes Travel_Rarely 896 Research & Development
## 107 977 48 No Travel_Rarely 277 Research & Development
## 108 978 39 Yes Travel_Rarely 360 Research & Development
## 109 979 35 No Travel_Rarely 607 Research & Development
## 110 980 49 No Travel_Rarely 1098 Research & Development
## 111 981 35 No Travel_Rarely 1402 Sales
## 112 982 46 No Travel_Rarely 1125 Sales
## 113 983 52 No Travel_Rarely 1323 Research & Development
## 114 984 26 No Travel_Rarely 1167 Sales
## 115 985 36 No Travel_Rarely 1120 Sales
## 116 986 32 Yes Travel_Frequently 238 Research & Development
## 117 987 22 Yes Travel_Frequently 1256 Research & Development
## 118 988 32 No Travel_Rarely 601 Sales
## 119 989 39 No Travel_Frequently 945 Research & Development
## 120 990 31 No Travel_Rarely 408 Research & Development
## 121 991 39 No Travel_Rarely 1383 Human Resources
## 122 992 24 No Travel_Rarely 581 Research & Development
## 123 993 30 No Travel_Rarely 1240 Human Resources
## 124 994 40 No Travel_Frequently 1184 Sales
## 125 995 38 No Non-Travel 152 Sales
## 126 996 34 No Travel_Rarely 665 Research & Development
## 127 997 38 No Travel_Frequently 1391 Research & Development
## 128 998 54 No Travel_Rarely 971 Research & Development
## 129 999 35 No Travel_Rarely 882 Sales
## 130 1000 32 Yes Travel_Rarely 374 Research & Development
## 131 1001 46 No Travel_Rarely 945 Human Resources
## 132 1002 47 No Travel_Frequently 1309 Sales
## 133 1003 32 No Travel_Rarely 977 Research & Development
## 134 1004 47 No Travel_Rarely 983 Research & Development
## 135 1005 48 No Travel_Rarely 1108 Research & Development
## 136 1006 34 No Travel_Rarely 735 Sales
## 137 1007 20 No Travel_Rarely 1141 Sales
## 138 1008 38 No Travel_Rarely 688 Research & Development
## 139 1009 53 No Travel_Rarely 1223 Research & Development
## 140 1010 53 No Travel_Rarely 1436 Sales
## 141 1011 37 Yes Travel_Frequently 504 Research & Development
## 142 1012 34 No Travel_Rarely 1031 Research & Development
## 143 1013 32 No Travel_Rarely 588 Sales
## 144 1014 34 Yes Travel_Frequently 234 Research & Development
## 145 1015 30 Yes Travel_Frequently 334 Sales
## 146 1016 43 No Travel_Rarely 990 Research & Development
## 147 1017 42 No Travel_Rarely 269 Research & Development
## 148 1018 35 No Travel_Rarely 662 Sales
## 149 1019 30 No Travel_Rarely 1339 Sales
## 150 1020 18 No Non-Travel 287 Research & Development
## 151 1021 45 No Travel_Rarely 561 Sales
## 152 1022 36 No Non-Travel 845 Sales
## 153 1023 39 No Travel_Rarely 835 Research & Development
## 154 1024 45 No Non-Travel 1238 Research & Development
## 155 1025 41 No Travel_Frequently 857 Research & Development
## 156 1026 39 No Non-Travel 105 Research & Development
## 157 1027 31 No Travel_Rarely 106 Human Resources
## 158 1028 25 No Travel_Rarely 891 Sales
## 159 1029 24 Yes Travel_Rarely 693 Sales
## 160 1030 44 Yes Travel_Rarely 1376 Human Resources
## 161 1031 32 No Travel_Rarely 499 Sales
## 162 1032 43 No Travel_Rarely 244 Human Resources
## 163 1033 27 No Travel_Rarely 269 Research & Development
## 164 1034 26 No Travel_Rarely 482 Research & Development
## 165 1035 40 No Non-Travel 663 Research & Development
## 166 1036 20 Yes Travel_Rarely 1362 Research & Development
## 167 1037 25 No Travel_Rarely 883 Sales
## 168 1038 54 No Travel_Rarely 397 Human Resources
## 169 1039 39 No Travel_Rarely 1253 Research & Development
## 170 1040 29 No Travel_Rarely 657 Research & Development
## 171 1041 31 Yes Travel_Frequently 307 Research & Development
## 172 1042 33 No Travel_Rarely 1198 Research & Development
## 173 1043 36 No Travel_Rarely 557 Sales
## 174 1044 36 No Travel_Rarely 928 Sales
## 175 1045 28 No Non-Travel 1103 Research & Development
## 176 1046 28 Yes Travel_Frequently 1009 Research & Development
## 177 1047 38 Yes Travel_Rarely 903 Research & Development
## 178 1048 33 No Travel_Frequently 553 Research & Development
## 179 1049 27 Yes Travel_Rarely 135 Research & Development
## 180 1050 35 Yes Travel_Rarely 763 Sales
## 181 1051 53 Yes Travel_Rarely 607 Research & Development
## 182 1052 36 No Travel_Frequently 635 Research & Development
## 183 1053 43 No Travel_Rarely 1473 Research & Development
## 184 1054 28 No Travel_Rarely 995 Research & Development
## 185 1055 27 No Travel_Frequently 591 Research & Development
## 186 1056 25 Yes Travel_Rarely 688 Research & Development
## 187 1057 27 No Travel_Frequently 472 Research & Development
## 188 1058 43 No Travel_Rarely 1034 Sales
## 189 1059 37 No Travel_Rarely 1239 Human Resources
## 190 1060 28 No Travel_Rarely 1451 Research & Development
## 191 1061 57 No Travel_Rarely 405 Research & Development
## 192 1062 34 No Travel_Frequently 426 Research & Development
## 193 1063 33 No Travel_Frequently 1296 Research & Development
## 194 1064 23 Yes Travel_Rarely 427 Sales
## 195 1065 58 No Non-Travel 390 Research & Development
## 196 1066 23 No Travel_Rarely 650 Research & Development
## 197 1067 40 No Travel_Rarely 1194 Research & Development
## 198 1068 60 No Travel_Rarely 422 Research & Development
## 199 1069 33 No Travel_Frequently 430 Sales
## 200 1070 50 No Travel_Rarely 316 Sales
## 201 1071 27 No Travel_Rarely 1291 Sales
## 202 1072 31 No Travel_Rarely 480 Research & Development
## 203 1073 20 No Travel_Rarely 654 Sales
## 204 1074 30 No Travel_Rarely 305 Research & Development
## 205 1075 56 No Travel_Rarely 1443 Sales
## 206 1076 46 No Travel_Rarely 1402 Sales
## 207 1077 53 No Travel_Rarely 1282 Research & Development
## 208 1078 31 No Travel_Rarely 311 Research & Development
## 209 1079 36 No Travel_Rarely 506 Research & Development
## 210 1080 42 No Travel_Frequently 532 Research & Development
## 211 1081 40 No Travel_Rarely 1322 Research & Development
## 212 1082 35 No Travel_Rarely 287 Research & Development
## 213 1083 23 Yes Travel_Frequently 638 Sales
## 214 1084 35 No Travel_Rarely 619 Sales
## 215 1085 38 No Travel_Frequently 216 Research & Development
## 216 1086 40 No Travel_Frequently 580 Sales
## 217 1087 31 No Travel_Rarely 1003 Sales
## 218 1088 38 No Travel_Rarely 268 Research & Development
## 219 1089 31 No Travel_Rarely 691 Research & Development
## 220 1090 27 No Travel_Rarely 205 Sales
## 221 1091 29 No Travel_Rarely 144 Sales
## 222 1092 36 No Non-Travel 301 Sales
## 223 1093 37 No Travel_Frequently 663 Research & Development
## 224 1094 40 No Travel_Rarely 107 Sales
## 225 1095 25 No Non-Travel 675 Research & Development
## 226 1096 37 No Travel_Rarely 446 Research & Development
## 227 1097 37 No Travel_Rarely 1017 Research & Development
## 228 1098 30 Yes Travel_Frequently 600 Human Resources
## 229 1099 32 No Travel_Rarely 1062 Research & Development
## 230 1100 37 Yes Travel_Rarely 370 Research & Development
## 231 1101 40 No Travel_Rarely 989 Research & Development
## 232 1102 40 No Travel_Rarely 759 Sales
## 233 1103 38 No Non-Travel 1327 Sales
## 234 1104 53 No Travel_Rarely 661 Sales
## 235 1105 36 No Travel_Rarely 188 Research & Development
## 236 1106 29 No Travel_Frequently 490 Research & Development
## 237 1107 27 No Travel_Rarely 1103 Research & Development
## 238 1108 44 No Travel_Rarely 1112 Human Resources
## 239 1109 27 No Travel_Frequently 1242 Sales
## 240 1110 35 No Travel_Rarely 682 Sales
## 241 1111 35 No Non-Travel 208 Research & Development
## 242 1112 59 No Travel_Rarely 1089 Sales
## 243 1113 33 Yes Travel_Rarely 1277 Research & Development
## 244 1114 50 Yes Travel_Frequently 562 Sales
## 245 1115 47 No Travel_Rarely 1180 Research & Development
## 246 1116 27 No Travel_Frequently 1131 Research & Development
## 247 1117 37 No Travel_Rarely 1305 Research & Development
## 248 1118 28 No Non-Travel 1476 Research & Development
## 249 1119 58 No Travel_Rarely 1272 Research & Development
## 250 1120 34 No Travel_Rarely 204 Sales
## 251 1121 50 Yes Travel_Rarely 410 Sales
## 252 1122 48 No Non-Travel 1262 Research & Development
## 253 1123 31 No Travel_Frequently 1125 Research & Development
## 254 1124 45 No Non-Travel 1195 Research & Development
## 255 1125 34 No Travel_Rarely 1320 Research & Development
## 256 1126 19 Yes Travel_Frequently 602 Sales
## 257 1127 24 Yes Travel_Frequently 1287 Research & Development
## 258 1128 52 No Travel_Rarely 1325 Research & Development
## 259 1129 41 No Travel_Rarely 334 Sales
## 260 1130 22 No Travel_Rarely 217 Research & Development
## 261 1131 31 No Travel_Frequently 1327 Research & Development
## 262 1132 39 No Travel_Rarely 412 Research & Development
## 263 1133 42 No Travel_Rarely 622 Research & Development
## 264 1134 38 No Travel_Frequently 148 Research & Development
## 265 1135 32 Yes Travel_Rarely 1033 Research & Development
## 266 1136 26 No Travel_Rarely 192 Research & Development
## 267 1137 37 No Travel_Rarely 921 Research & Development
## 268 1138 41 No Travel_Frequently 1018 Sales
## 269 1139 38 No Travel_Rarely 119 Sales
## 270 1140 43 No Travel_Rarely 1291 Research & Development
## 271 1141 31 No Travel_Rarely 1463 Research & Development
## 272 1142 42 No Travel_Rarely 691 Sales
## 273 1143 48 No Travel_Rarely 1355 Research & Development
## 274 1144 28 No Travel_Rarely 821 Sales
## 275 1145 44 No Travel_Frequently 602 Human Resources
## 276 1146 45 No Travel_Rarely 374 Sales
## 277 1147 34 No Travel_Rarely 628 Research & Development
## 278 1148 31 No Travel_Rarely 471 Research & Development
## 279 1149 29 No Travel_Rarely 1010 Research & Development
## 280 1150 30 No Travel_Rarely 288 Research & Development
## 281 1151 38 No Travel_Frequently 693 Research & Development
## 282 1152 27 No Travel_Frequently 793 Sales
## 283 1153 54 No Travel_Frequently 928 Research & Development
## 284 1154 28 Yes Travel_Rarely 1485 Research & Development
## 285 1155 49 No Travel_Rarely 470 Research & Development
## 286 1156 51 No Travel_Rarely 313 Research & Development
## 287 1157 29 Yes Travel_Rarely 341 Sales
## 288 1158 36 No Travel_Rarely 430 Research & Development
## 289 1159 27 No Travel_Rarely 199 Research & Development
## 290 1160 36 No Travel_Rarely 216 Research & Development
## 291 1161 44 Yes Travel_Rarely 1097 Research & Development
## 292 1162 38 No Travel_Rarely 168 Research & Development
## 293 1163 29 No Travel_Frequently 574 Research & Development
## 294 1164 37 No Travel_Rarely 799 Research & Development
## 295 1165 26 Yes Travel_Frequently 887 Research & Development
## 296 1166 32 No Travel_Frequently 116 Research & Development
## 297 1167 40 No Travel_Rarely 1492 Research & Development
## 298 1168 30 No Travel_Rarely 1082 Sales
## 299 1169 27 No Travel_Rarely 798 Research & Development
## 300 1170 37 No Travel_Rarely 290 Research & Development
## DistanceFromHome Education EducationField EmployeeCount
## 1 2 4 Life Sciences 1
## 2 13 1 Life Sciences 1
## 3 13 4 Marketing 1
## 4 9 4 Life Sciences 1
## 5 19 3 Other 1
## 6 5 3 Technical Degree 1
## 7 22 2 Marketing 1
## 8 10 2 Life Sciences 1
## 9 23 3 Life Sciences 1
## 10 11 2 Technical Degree 1
## 11 9 3 Marketing 1
## 12 1 4 Life Sciences 1
## 13 3 2 Medical 1
## 14 8 1 Marketing 1
## 15 2 3 Medical 1
## 16 3 1 Life Sciences 1
## 17 29 1 Life Sciences 1
## 18 8 5 Life Sciences 1
## 19 7 4 Life Sciences 1
## 20 8 4 Technical Degree 1
## 21 2 3 Life Sciences 1
## 22 29 4 Technical Degree 1
## 23 9 2 Medical 1
## 24 3 1 Medical 1
## 25 7 4 Life Sciences 1
## 26 2 2 Life Sciences 1
## 27 16 3 Life Sciences 1
## 28 2 1 Life Sciences 1
## 29 15 2 Medical 1
## 30 2 3 Life Sciences 1
## 31 2 1 Medical 1
## 32 1 5 Marketing 1
## 33 16 4 Life Sciences 1
## 34 1 4 Life Sciences 1
## 35 11 2 Life Sciences 1
## 36 7 3 Life Sciences 1
## 37 1 3 Medical 1
## 38 5 2 Medical 1
## 39 5 4 Technical Degree 1
## 40 1 1 Life Sciences 1
## 41 3 3 Life Sciences 1
## 42 1 4 Other 1
## 43 2 2 Life Sciences 1
## 44 5 3 Medical 1
## 45 7 2 Medical 1
## 46 20 1 Life Sciences 1
## 47 1 3 Medical 1
## 48 10 3 Medical 1
## 49 10 1 Medical 1
## 50 3 3 Medical 1
## 51 7 1 Marketing 1
## 52 8 1 Life Sciences 1
## 53 15 3 Other 1
## 54 2 5 Medical 1
## 55 22 5 Medical 1
## 56 10 4 Life Sciences 1
## 57 8 3 Medical 1
## 58 10 3 Medical 1
## 59 16 2 Medical 1
## 60 2 2 Life Sciences 1
## 61 10 3 Life Sciences 1
## 62 3 2 Life Sciences 1
## 63 1 3 Marketing 1
## 64 2 1 Life Sciences 1
## 65 4 3 Technical Degree 1
## 66 9 3 Medical 1
## 67 1 4 Medical 1
## 68 8 4 Life Sciences 1
## 69 24 1 Life Sciences 1
## 70 3 3 Medical 1
## 71 9 4 Life Sciences 1
## 72 6 3 Medical 1
## 73 25 3 Life Sciences 1
## 74 2 4 Life Sciences 1
## 75 10 3 Medical 1
## 76 2 1 Life Sciences 1
## 77 7 1 Marketing 1
## 78 23 3 Life Sciences 1
## 79 17 4 Life Sciences 1
## 80 27 2 Life Sciences 1
## 81 10 4 Technical Degree 1
## 82 26 3 Marketing 1
## 83 3 3 Other 1
## 84 5 2 Life Sciences 1
## 85 9 3 Life Sciences 1
## 86 4 3 Life Sciences 1
## 87 7 3 Life Sciences 1
## 88 29 4 Medical 1
## 89 23 2 Life Sciences 1
## 90 2 4 Life Sciences 1
## 91 15 3 Life Sciences 1
## 92 24 2 Technical Degree 1
## 93 12 3 Life Sciences 1
## 94 3 3 Human Resources 1
## 95 7 1 Medical 1
## 96 7 2 Medical 1
## 97 28 4 Medical 1
## 98 2 2 Medical 1
## 99 2 3 Medical 1
## 100 16 4 Medical 1
## 101 9 3 Marketing 1
## 102 12 3 Medical 1
## 103 1 4 Medical 1
## 104 1 3 Life Sciences 1
## 105 17 1 Medical 1
## 106 18 1 Medical 1
## 107 6 3 Life Sciences 1
## 108 23 3 Medical 1
## 109 9 3 Life Sciences 1
## 110 4 2 Medical 1
## 111 28 4 Life Sciences 1
## 112 10 3 Marketing 1
## 113 2 3 Life Sciences 1
## 114 5 3 Other 1
## 115 11 4 Marketing 1
## 116 5 2 Life Sciences 1
## 117 3 4 Life Sciences 1
## 118 7 5 Marketing 1
## 119 22 3 Medical 1
## 120 9 4 Life Sciences 1
## 121 2 3 Life Sciences 1
## 122 9 3 Medical 1
## 123 9 3 Human Resources 1
## 124 2 4 Medical 1
## 125 10 3 Technical Degree 1
## 126 6 4 Other 1
## 127 10 1 Medical 1
## 128 1 3 Medical 1
## 129 3 4 Life Sciences 1
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## MaritalStatus MonthlyRate NumCompaniesWorked Over18 OverTime
## 1 Married 19246 1 Y No
## 2 Single 17241 3 Y No
## 3 Single 9277 6 Y Yes
## 4 Married 9238 1 Y No
## 5 Divorced 16290 1 Y No
## 6 Married 3335 3 Y No
## 7 Married 15480 0 Y No
## 8 Single 25308 1 Y No
## 9 Divorced 22310 1 Y No
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## 13 Married 3487 1 Y No
## 14 Married 24008 0 Y No
## 15 Married 3687 2 Y No
## 16 Married 10022 0 Y Yes
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## 28 Married 26582 0 Y Yes
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## 30 Married 11868 4 Y No
## 31 Married 16632 9 Y No
## 32 Married 9282 7 Y No
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## 35 Married 5404 3 Y No
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## 110 Married 12124 2 Y No
## 111 Married 26204 0 Y No
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## 122 Married 17616 1 Y No
## 123 Married 13982 0 Y Yes
## 124 Married 25440 5 Y No
## 125 Single 19899 1 Y Yes
## 126 Single 12482 1 Y No
## 127 Married 12127 0 Y No
## 128 Single 5652 6 Y No
## 129 Divorced 6812 6 Y No
## 130 Single 18016 1 Y Yes
## 131 Divorced 10425 8 Y Yes
## 132 Single 25751 3 Y No
## 133 Divorced 25518 0 Y No
## 134 Divorced 7389 5 Y No
## 135 Married 16530 8 Y No
## 136 Married 16495 3 Y Yes
## 137 Single 13251 1 Y No
## 138 Divorced 18899 9 Y No
## 139 Divorced 18640 3 Y No
## 140 Married 16047 2 Y Yes
## 141 Divorced 22573 6 Y No
## 142 Divorced 15000 6 Y No
## 143 Married 24624 1 Y Yes
## 144 Married 6208 4 Y No
## 145 Single 22967 5 Y No
## 146 Divorced 5855 5 Y No
## 147 Divorced 8007 0 Y Yes
## 148 Married 11439 1 Y No
## 149 Married 8053 2 Y No
## 150 Single 13493 1 Y No
## 151 Married 16177 0 Y No
## 152 Single 15276 4 Y No
## 153 Divorced 5141 8 Y No
## 154 Married 3395 3 Y No
## 155 Divorced 12888 4 Y No
## 156 Single 15302 2 Y No
## 157 Married 17822 3 Y No
## 158 Single 12090 1 Y Yes
## 159 Single 24785 9 Y No
## 160 Married 2326 9 Y No
## 161 Married 20497 0 Y Yes
## 162 Single 5033 0 Y No
## 163 Divorced 8842 1 Y Yes
## 164 Married 14908 1 Y Yes
## 165 Divorced 23099 3 Y No
## 166 Single 26999 1 Y Yes
## 167 Single 22807 1 Y No
## 168 Married 6729 2 Y No
## 169 Single 7914 7 Y No
## 170 Married 25549 4 Y No
## 171 Single 11652 0 Y No
## 172 Single 3339 3 Y Yes
## 173 Married 12695 0 Y No
## 174 Married 2823 1 Y Yes
## 175 Single 2122 1 Y No
## 176 Divorced 7160 1 Y No
## 177 Married 7653 4 Y No
## 178 Married 23364 6 Y No
## 179 Single 25681 1 Y Yes
## 180 Divorced 22098 6 Y Yes
## 181 Married 14618 0 Y No
## 182 Single 7703 1 Y No
## 183 Divorced 2227 4 Y Yes
## 184 Divorced 9834 5 Y No
## 185 Single 6297 2 Y No
## 186 Married 9396 5 Y No
## 187 Married 9679 5 Y No
## 188 Married 7744 5 Y Yes
## 189 Divorced 12832 2 Y No
## 190 Married 19911 0 Y No
## 191 Married 2721 0 Y No
## 192 Divorced 17000 1 Y No
## 193 Divorced 5335 3 Y No
## 194 Divorced 25103 1 Y Yes
## 195 Divorced 17056 2 Y Yes
## 196 Married 4344 1 Y No
## 197 Married 12549 2 Y No
## 198 Married 3854 5 Y No
## 199 Married 17456 0 Y No
## 200 Married 9983 7 Y No
## 201 Married 6527 8 Y No
## 202 Married 4156 1 Y No
## 203 Single 5050 1 Y No
## 204 Married 9128 3 Y No
## 205 Married 20328 4 Y No
## 206 Married 24097 8 Y No
## 207 Divorced 10735 4 Y No
## 208 Divorced 18624 1 Y No
## 209 Single 26285 4 Y No
## 210 Divorced 12932 2 Y No
## 211 Single 2725 2 Y No
## 212 Married 8952 1 Y No
## 213 Married 26956 1 Y No
## 214 Married 18659 9 Y No
## 215 Single 8787 0 Y No
## 216 Married 23772 5 Y Yes
## 217 Married 20943 1 Y No
## 218 Married 20471 6 Y Yes
## 219 Married 10077 0 Y Yes
## 220 Married 7100 1 Y Yes
## 221 Divorced 11866 1 Y Yes
## 222 Divorced 10436 1 Y No
## 223 Divorced 10056 1 Y Yes
## 224 Divorced 11591 7 Y No
## 225 Divorced 18384 1 Y No
## 226 Married 15701 6 Y No
## 227 Married 18697 2 Y No
## 228 Divorced 9732 6 Y No
## 229 Married 3956 1 Y No
## 230 Single 4992 1 Y No
## 231 Married 6499 1 Y No
## 232 Divorced 22553 7 Y No
## 233 Married 19682 3 Y No
## 234 Married 20715 7 Y Yes
## 235 Single 23293 2 Y No
## 236 Divorced 17940 0 Y No
## 237 Married 14377 1 Y Yes
## 238 Single 26894 4 Y No
## 239 Single 12916 1 Y No
## 240 Married 15975 0 Y No
## 241 Married 12250 1 Y No
## 242 Married 11038 3 Y Yes
## 243 Married 24619 7 Y Yes
## 244 Married 23452 3 Y Yes
## 245 Single 13084 2 Y No
## 246 Married 23844 0 Y No
## 247 Single 21123 2 Y Yes
## 248 Married 16392 0 Y No
## 249 Divorced 9255 2 Y Yes
## 250 Divorced 2912 1 Y Yes
## 251 Divorced 16586 4 Y Yes
## 252 Single 16154 2 Y No
## 253 Married 5771 1 Y No
## 254 Married 20462 9 Y No
## 255 Married 21643 0 Y No
## 256 Single 20989 0 Y No
## 257 Married 14168 1 Y Yes
## 258 Married 21821 8 Y No
## 259 Single 15896 1 Y No
## 260 Married 6881 1 Y No
## 261 Divorced 13523 9 Y No
## 262 Divorced 17334 6 Y Yes
## 263 Married 5718 1 Y No
## 264 Single 23826 1 Y No
## 265 Single 10224 7 Y No
## 266 Married 11141 1 Y No
## 267 Married 17663 6 Y No
## 268 Divorced 4297 0 Y No
## 269 Divorced 9647 0 Y No
## 270 Married 3525 1 Y No
## 271 Married 14408 0 Y No
## 272 Married 21173 0 Y No
## 273 Single 22245 7 Y No
## 274 Single 24252 1 Y No
## 275 Married 10503 4 Y Yes
## 276 Single 23333 8 Y No
## 277 Married 10228 2 Y No
## 278 Divorced 16031 8 Y No
## 279 Divorced 5598 1 Y No
## 280 Married 15830 1 Y No
## 281 Divorced 15748 1 Y No
## 282 Single 20392 3 Y No
## 283 Single 16885 3 Y No
## 284 Married 22955 1 Y Yes
## 285 Married 5549 1 Y No
## 286 Single 7192 3 Y No
## 287 Divorced 23522 6 Y Yes
## 288 Married 19573 4 Y Yes
## 289 Married 7950 1 Y No
## 290 Divorced 2819 6 Y No
## 291 Single 10826 1 Y Yes
## 292 Single 15397 4 Y Yes
## 293 Married 7003 1 Y No
## 294 Single 23848 4 Y No
## 295 Married 20898 1 Y Yes
## 296 Married 7331 1 Y No
## 297 Married 26542 4 Y No
## 298 Single 19558 0 Y No
## 299 Divorced 5013 0 Y No
## 300 Married 22977 1 Y Yes
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 20 4 3
## 2 18 3 1
## 3 17 3 3
## 4 22 4 2
## 5 11 3 1
## 6 14 3 3
## 7 13 3 1
## 8 20 4 3
## 9 25 4 3
## 10 16 3 2
## 11 11 3 1
## 12 22 4 3
## 13 14 3 1
## 14 19 3 2
## 15 19 3 2
## 16 19 3 1
## 17 17 3 4
## 18 14 3 3
## 19 22 4 4
## 20 17 3 2
## 21 13 3 3
## 22 12 3 2
## 23 16 3 4
## 24 20 4 2
## 25 14 3 2
## 26 14 3 3
## 27 20 4 3
## 28 13 3 4
## 29 12 3 4
## 30 13 3 2
## 31 12 3 4
## 32 12 3 3
## 33 14 3 3
## 34 19 3 3
## 35 13 3 2
## 36 17 3 3
## 37 13 3 3
## 38 16 3 3
## 39 13 3 3
## 40 13 3 4
## 41 14 3 2
## 42 11 3 2
## 43 17 3 4
## 44 12 3 4
## 45 14 3 2
## 46 14 3 2
## 47 25 4 3
## 48 13 3 1
## 49 22 4 2
## 50 20 4 1
## 51 14 3 1
## 52 19 3 4
## 53 14 3 2
## 54 16 3 1
## 55 25 4 3
## 56 13 3 3
## 57 15 3 3
## 58 12 3 1
## 59 13 3 2
## 60 13 3 3
## 61 14 3 3
## 62 15 3 4
## 63 16 3 3
## 64 14 3 4
## 65 19 3 2
## 66 11 3 3
## 67 16 3 3
## 68 15 3 4
## 69 13 3 1
## 70 19 3 4
## 71 14 3 2
## 72 11 3 1
## 73 11 3 4
## 74 18 3 2
## 75 12 3 1
## 76 19 3 4
## 77 13 3 2
## 78 22 4 2
## 79 11 3 2
## 80 12 3 4
## 81 22 4 2
## 82 14 3 2
## 83 15 3 1
## 84 14 3 3
## 85 15 3 3
## 86 21 4 3
## 87 11 3 1
## 88 12 3 2
## 89 16 3 4
## 90 12 3 4
## 91 19 3 1
## 92 17 3 1
## 93 15 3 3
## 94 15 3 3
## 95 11 3 3
## 96 12 3 4
## 97 14 3 2
## 98 13 3 2
## 99 22 4 3
## 100 13 3 4
## 101 14 3 1
## 102 11 3 3
## 103 11 3 3
## 104 13 3 4
## 105 15 3 2
## 106 13 3 3
## 107 13 3 4
## 108 13 3 1
## 109 20 4 2
## 110 13 3 3
## 111 23 4 1
## 112 19 3 3
## 113 15 3 2
## 114 18 3 4
## 115 13 3 1
## 116 14 3 1
## 117 11 3 2
## 118 12 3 3
## 119 13 3 3
## 120 16 3 4
## 121 11 3 3
## 122 16 3 4
## 123 19 3 4
## 124 12 3 4
## 125 13 3 2
## 126 14 3 3
## 127 17 3 2
## 128 19 3 4
## 129 13 3 2
## 130 24 4 3
## 131 22 4 4
## 132 19 3 1
## 133 13 3 3
## 134 13 3 3
## 135 12 3 4
## 136 12 3 3
## 137 19 3 1
## 138 14 3 2
## 139 18 3 2
## 140 20 4 4
## 141 11 3 2
## 142 15 3 3
## 143 11 3 4
## 144 17 3 3
## 145 15 3 3
## 146 12 3 3
## 147 11 3 3
## 148 13 3 1
## 149 12 3 3
## 150 15 3 4
## 151 19 3 2
## 152 15 3 2
## 153 14 3 2
## 154 23 4 4
## 155 13 3 2
## 156 13 3 3
## 157 12 3 4
## 158 11 3 2
## 159 14 3 1
## 160 14 3 4
## 161 13 3 1
## 162 13 3 2
## 163 16 3 2
## 164 13 3 3
## 165 11 3 3
## 166 11 3 4
## 167 23 4 2
## 168 15 3 3
## 169 21 4 3
## 170 12 3 1
## 171 11 3 2
## 172 11 3 2
## 173 19 3 3
## 174 14 3 4
## 175 14 3 3
## 176 15 3 1
## 177 11 3 1
## 178 11 3 2
## 179 13 3 4
## 180 14 3 4
## 181 16 3 2
## 182 13 3 1
## 183 14 3 4
## 184 18 3 2
## 185 13 3 3
## 186 13 3 3
## 187 19 3 3
## 188 22 4 3
## 189 13 3 3
## 190 17 3 1
## 191 24 4 1
## 192 13 3 1
## 193 23 4 3
## 194 21 4 2
## 195 13 3 4
## 196 14 3 4
## 197 14 3 2
## 198 11 3 4
## 199 14 3 1
## 200 15 3 4
## 201 14 3 2
## 202 12 3 2
## 203 17 3 4
## 204 16 3 3
## 205 16 3 3
## 206 23 4 1
## 207 11 3 4
## 208 11 3 1
## 209 12 3 4
## 210 11 3 2
## 211 14 3 4
## 212 12 3 4
## 213 19 3 1
## 214 11 3 3
## 215 21 4 2
## 216 21 4 3
## 217 19 3 3
## 218 13 3 2
## 219 12 3 3
## 220 11 3 4
## 221 14 3 1
## 222 24 4 1
## 223 14 3 3
## 224 12 3 2
## 225 12 3 4
## 226 12 3 2
## 227 14 3 1
## 228 11 3 3
## 229 13 3 3
## 230 15 3 2
## 231 14 3 2
## 232 12 3 3
## 233 18 3 4
## 234 18 3 4
## 235 18 3 3
## 236 14 3 4
## 237 14 3 4
## 238 11 3 2
## 239 14 3 4
## 240 16 3 4
## 241 12 3 4
## 242 14 3 3
## 243 12 3 4
## 244 14 3 1
## 245 23 4 2
## 246 19 3 4
## 247 12 3 4
## 248 11 3 1
## 249 13 3 4
## 250 18 3 4
## 251 13 3 2
## 252 22 4 3
## 253 21 4 2
## 254 23 4 4
## 255 17 3 4
## 256 21 4 1
## 257 16 3 4
## 258 20 4 2
## 259 19 3 2
## 260 15 3 1
## 261 12 3 1
## 262 13 3 4
## 263 13 3 2
## 264 22 4 2
## 265 22 4 1
## 266 16 3 1
## 267 20 4 2
## 268 17 3 4
## 269 12 3 3
## 270 24 4 1
## 271 21 4 2
## 272 11 3 4
## 273 14 3 2
## 274 14 3 2
## 275 11 3 3
## 276 15 3 3
## 277 12 3 1
## 278 12 3 2
## 279 15 3 1
## 280 19 3 1
## 281 11 3 4
## 282 20 4 2
## 283 12 3 4
## 284 11 3 4
## 285 14 3 3
## 286 18 3 3
## 287 19 3 3
## 288 22 4 4
## 289 13 3 3
## 290 20 4 4
## 291 11 3 3
## 292 14 3 4
## 293 13 3 2
## 294 17 3 4
## 295 14 3 1
## 296 20 4 3
## 297 20 4 4
## 298 11 3 2
## 299 12 3 3
## 300 12 3 1
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## 1 80 1 7 5
## 2 80 0 5 4
## 3 80 0 24 2
## 4 80 0 5 3
## 5 80 2 7 3
## 6 80 1 19 6
## 7 80 1 8 2
## 8 80 0 2 2
## 9 80 3 10 2
## 10 80 0 29 1
## 11 80 1 28 1
## 12 80 1 7 3
## 13 80 1 6 5
## 14 80 1 5 3
## 15 80 2 10 2
## 16 80 1 4 3
## 17 80 2 11 3
## 18 80 1 7 3
## 19 80 1 10 3
## 20 80 0 6 2
## 21 80 0 9 4
## 22 80 1 9 3
## 23 80 0 9 3
## 24 80 2 6 3
## 25 80 2 29 3
## 26 80 1 12 4
## 27 80 0 11 2
## 28 80 0 21 3
## 29 80 0 10 6
## 30 80 1 6 3
## 31 80 1 6 3
## 32 80 1 11 4
## 33 80 1 18 2
## 34 80 2 8 3
## 35 80 1 26 4
## 36 80 1 4 3
## 37 80 1 21 5
## 38 80 2 8 2
## 39 80 0 2 3
## 40 80 0 1 2
## 41 80 1 25 2
## 42 80 1 7 2
## 43 80 1 7 5
## 44 80 0 14 6
## 45 80 0 8 6
## 46 80 0 5 2
## 47 80 1 3 3
## 48 80 0 5 4
## 49 80 0 1 5
## 50 80 3 12 3
## 51 80 0 4 3
## 52 80 0 1 2
## 53 80 1 9 3
## 54 80 1 10 3
## 55 80 1 36 6
## 56 80 1 10 3
## 57 80 2 10 4
## 58 80 1 20 3
## 59 80 2 1 2
## 60 80 0 8 2
## 61 80 1 15 3
## 62 80 0 10 6
## 63 80 1 10 2
## 64 80 1 3 3
## 65 80 0 1 2
## 66 80 0 4 2
## 67 80 1 17 3
## 68 80 0 18 2
## 69 80 0 18 2
## 70 80 1 1 3
## 71 80 0 15 3
## 72 80 1 11 3
## 73 80 0 28 3
## 74 80 1 4 2
## 75 80 0 0 2
## 76 80 1 6 0
## 77 80 0 1 3
## 78 80 2 3 3
## 79 80 1 8 2
## 80 80 0 8 2
## 81 80 2 10 1
## 82 80 1 8 3
## 83 80 0 8 3
## 84 80 0 6 3
## 85 80 0 9 2
## 86 80 2 9 2
## 87 80 0 37 3
## 88 80 0 10 2
## 89 80 0 21 2
## 90 80 0 40 2
## 91 80 0 6 1
## 92 80 2 9 3
## 93 80 1 13 3
## 94 80 1 12 3
## 95 80 2 2 2
## 96 80 1 29 2
## 97 80 0 5 3
## 98 80 2 10 2
## 99 80 1 32 3
## 100 80 1 5 2
## 101 80 0 7 5
## 102 80 2 10 2
## 103 80 0 5 2
## 104 80 0 1 0
## 105 80 2 5 6
## 106 80 0 4 3
## 107 80 0 19 0
## 108 80 0 6 2
## 109 80 1 17 2
## 110 80 1 23 2
## 111 80 2 6 5
## 112 80 1 15 3
## 113 80 0 6 3
## 114 80 0 5 2
## 115 80 1 8 2
## 116 80 0 8 2
## 117 80 1 1 5
## 118 80 1 7 3
## 119 80 0 21 2
## 120 80 0 3 5
## 121 80 2 13 2
## 122 80 1 5 1
## 123 80 0 12 2
## 124 80 3 5 2
## 125 80 0 6 1
## 126 80 0 16 3
## 127 80 1 3 0
## 128 80 0 29 3
## 129 80 1 12 2
## 130 80 0 1 2
## 131 80 1 16 2
## 132 80 0 5 3
## 133 80 2 10 4
## 134 80 3 20 2
## 135 80 1 10 3
## 136 80 0 9 3
## 137 80 0 2 3
## 138 80 1 10 2
## 139 80 1 26 6
## 140 80 1 13 3
## 141 80 2 17 5
## 142 80 1 12 3
## 143 80 0 13 2
## 144 80 1 11 3
## 145 80 0 9 5
## 146 80 1 8 0
## 147 80 1 10 4
## 148 80 2 10 3
## 149 80 1 12 2
## 150 80 0 0 2
## 151 80 1 9 3
## 152 80 0 7 6
## 153 80 3 7 2
## 154 80 1 25 3
## 155 80 1 21 2
## 156 80 0 21 3
## 157 80 0 9 1
## 158 80 0 5 3
## 159 80 0 4 3
## 160 80 1 24 1
## 161 80 3 4 3
## 162 80 0 10 5
## 163 80 1 9 3
## 164 80 1 1 3
## 165 80 2 11 2
## 166 80 0 1 5
## 167 80 0 6 5
## 168 80 1 16 1
## 169 80 0 9 3
## 170 80 1 11 3
## 171 80 0 6 2
## 172 80 0 6 1
## 173 80 2 10 2
## 174 80 1 18 1
## 175 80 0 5 3
## 176 80 2 1 2
## 177 80 2 7 2
## 178 80 2 12 2
## 179 80 0 8 2
## 180 80 2 7 2
## 181 80 1 34 4
## 182 80 0 8 2
## 183 80 0 8 3
## 184 80 1 6 2
## 185 80 0 6 0
## 186 80 1 6 5
## 187 80 1 6 1
## 188 80 1 22 3
## 189 80 0 19 4
## 190 80 0 6 2
## 191 80 1 13 2
## 192 80 1 9 3
## 193 80 1 15 2
## 194 80 1 3 2
## 195 80 1 12 2
## 196 80 1 5 2
## 197 80 3 20 2
## 198 80 0 33 5
## 199 80 2 5 2
## 200 80 1 4 2
## 201 80 1 5 4
## 202 80 1 13 5
## 203 80 0 2 2
## 204 80 1 10 3
## 205 80 1 6 3
## 206 80 0 28 2
## 207 80 1 26 3
## 208 80 1 10 2
## 209 80 0 10 2
## 210 80 1 19 3
## 211 80 0 8 2
## 212 80 1 4 5
## 213 80 1 1 3
## 214 80 0 15 2
## 215 80 0 10 2
## 216 80 1 20 2
## 217 80 1 6 3
## 218 80 1 6 0
## 219 80 1 6 4
## 220 80 0 6 3
## 221 80 2 7 2
## 222 80 1 15 4
## 223 80 3 10 1
## 224 80 1 7 2
## 225 80 2 6 2
## 226 80 1 8 2
## 227 80 1 17 2
## 228 80 1 6 0
## 229 80 1 8 4
## 230 80 0 10 4
## 231 80 1 21 2
## 232 80 2 8 5
## 233 80 1 13 0
## 234 80 1 35 3
## 235 80 0 8 6
## 236 80 2 8 2
## 237 80 2 9 3
## 238 80 0 10 1
## 239 80 0 7 2
## 240 80 1 6 2
## 241 80 1 15 5
## 242 80 1 14 1
## 243 80 0 15 2
## 244 80 1 18 4
## 245 80 0 25 3
## 246 80 1 8 2
## 247 80 0 18 2
## 248 80 3 10 6
## 249 80 1 24 3
## 250 80 2 8 3
## 251 80 1 20 3
## 252 80 0 27 3
## 253 80 0 10 6
## 254 80 1 22 1
## 255 80 0 2 5
## 256 80 0 1 5
## 257 80 1 6 4
## 258 80 1 9 3
## 259 80 0 22 2
## 260 80 1 4 3
## 261 80 1 9 3
## 262 80 2 21 4
## 263 80 2 10 4
## 264 80 0 4 3
## 265 80 0 10 2
## 266 80 2 6 2
## 267 80 1 17 3
## 268 80 1 10 2
## 269 80 2 17 6
## 270 80 1 14 3
## 271 80 1 10 2
## 272 80 1 10 2
## 273 80 0 27 3
## 274 80 0 4 3
## 275 80 0 14 3
## 276 80 0 8 3
## 277 80 0 6 3
## 278 80 1 4 0
## 279 80 3 3 5
## 280 80 3 11 3
## 281 80 3 4 2
## 282 80 0 8 3
## 283 80 0 20 4
## 284 80 0 1 4
## 285 80 0 16 2
## 286 80 0 21 6
## 287 80 3 5 3
## 288 80 1 15 2
## 289 80 1 4 0
## 290 80 2 7 0
## 291 80 0 6 4
## 292 80 0 10 4
## 293 80 0 11 3
## 294 80 0 12 3
## 295 80 1 8 2
## 296 80 1 2 2
## 297 80 1 14 6
## 298 80 0 6 6
## 299 80 2 6 5
## 300 80 1 8 3
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 3 7 7
## 2 3 3 2
## 3 2 19 7
## 4 3 5 4
## 5 3 7 7
## 6 4 1 0
## 7 4 7 7
## 8 1 2 2
## 9 2 10 7
## 10 2 5 2
## 11 4 7 7
## 12 3 4 2
## 13 2 6 0
## 14 3 4 2
## 15 3 0 0
## 16 4 3 2
## 17 3 7 0
## 18 3 0 0
## 19 2 7 7
## 20 1 5 4
## 21 3 9 8
## 22 3 4 2
## 23 3 9 8
## 24 3 6 5
## 25 3 8 7
## 26 2 9 8
## 27 2 0 0
## 28 2 20 8
## 29 4 10 8
## 30 3 2 2
## 31 3 2 2
## 32 2 1 0
## 33 3 8 7
## 34 2 1 0
## 35 4 9 3
## 36 3 3 2
## 37 2 10 9
## 38 2 6 2
## 39 2 2 2
## 40 3 1 0
## 41 3 8 7
## 42 3 6 2
## 43 2 4 3
## 44 3 11 10
## 45 3 2 2
## 46 3 4 3
## 47 3 3 2
## 48 3 5 3
## 49 3 1 0
## 50 2 1 0
## 51 3 3 2
## 52 3 1 0
## 53 2 1 0
## 54 4 4 3
## 55 3 10 8
## 56 3 7 7
## 57 4 10 7
## 58 3 18 16
## 59 2 1 0
## 60 2 7 7
## 61 3 15 14
## 62 3 7 7
## 63 3 10 7
## 64 2 1 0
## 65 3 1 0
## 66 2 0 0
## 67 3 4 2
## 68 3 4 2
## 69 3 7 7
## 70 3 1 0
## 71 1 5 4
## 72 3 7 6
## 73 2 21 16
## 74 3 3 2
## 75 3 0 0
## 76 4 6 4
## 77 3 1 0
## 78 1 3 2
## 79 2 7 7
## 80 3 5 4
## 81 2 6 3
## 82 2 7 7
## 83 2 4 3
## 84 2 5 4
## 85 2 5 4
## 86 4 5 0
## 87 2 6 4
## 88 3 8 7
## 89 3 2 0
## 90 3 31 15
## 91 3 6 5
## 92 3 9 8
## 93 2 4 3
## 94 1 8 3
## 95 3 2 2
## 96 2 27 3
## 97 1 5 4
## 98 3 10 9
## 99 3 2 2
## 100 3 3 2
## 101 3 7 7
## 102 1 9 7
## 103 2 4 2
## 104 4 1 0
## 105 3 4 2
## 106 2 4 3
## 107 3 2 2
## 108 3 5 2
## 109 3 17 14
## 110 4 1 0
## 111 3 5 3
## 112 3 3 2
## 113 2 2 2
## 114 3 4 2
## 115 2 6 3
## 116 3 4 1
## 117 3 0 0
## 118 2 4 3
## 119 3 21 6
## 120 3 2 2
## 121 3 5 4
## 122 3 5 3
## 123 1 11 9
## 124 3 0 0
## 125 3 5 3
## 126 3 16 13
## 127 2 2 1
## 128 2 20 7
## 129 3 10 9
## 130 1 1 0
## 131 3 4 2
## 132 3 0 0
## 133 2 9 5
## 134 3 5 0
## 135 2 8 2
## 136 2 4 2
## 137 3 2 2
## 138 3 2 2
## 139 3 7 7
## 140 1 7 7
## 141 3 1 0
## 142 3 1 0
## 143 3 13 12
## 144 2 7 1
## 145 2 6 3
## 146 3 6 4
## 147 3 9 6
## 148 3 10 8
## 149 3 10 9
## 150 3 0 0
## 151 4 8 7
## 152 3 1 0
## 153 3 2 2
## 154 2 23 15
## 155 2 7 6
## 156 2 6 0
## 157 3 2 2
## 158 3 5 4
## 159 3 2 2
## 160 3 20 6
## 161 2 3 2
## 162 3 9 7
## 163 3 9 8
## 164 2 1 0
## 165 4 8 7
## 166 3 1 0
## 167 2 6 5
## 168 4 9 7
## 169 3 4 3
## 170 2 8 7
## 171 4 5 4
## 172 3 3 2
## 173 3 9 7
## 174 2 18 14
## 175 2 5 3
## 176 3 1 0
## 177 3 5 2
## 178 3 10 6
## 179 3 8 2
## 180 2 2 2
## 181 3 33 7
## 182 3 8 1
## 183 3 5 2
## 184 3 2 2
## 185 2 4 2
## 186 3 2 2
## 187 3 2 2
## 188 3 17 13
## 189 2 10 0
## 190 1 5 3
## 191 2 12 9
## 192 3 9 7
## 193 1 13 11
## 194 3 3 2
## 195 3 5 3
## 196 4 4 3
## 197 3 5 3
## 198 1 29 8
## 199 3 4 3
## 200 3 2 2
## 201 3 1 0
## 202 1 13 10
## 203 3 2 1
## 204 3 7 0
## 205 3 0 0
## 206 3 26 15
## 207 2 14 13
## 208 3 10 8
## 209 3 8 0
## 210 3 5 4
## 211 3 2 2
## 212 3 4 3
## 213 2 1 0
## 214 3 11 9
## 215 3 9 7
## 216 3 18 13
## 217 3 5 2
## 218 1 1 0
## 219 3 5 2
## 220 3 6 2
## 221 3 7 7
## 222 2 15 12
## 223 3 10 8
## 224 4 5 1
## 225 3 6 3
## 226 2 6 5
## 227 2 3 1
## 228 2 4 2
## 229 3 8 0
## 230 1 10 3
## 231 3 20 8
## 232 3 5 4
## 233 3 8 7
## 234 3 5 2
## 235 3 6 2
## 236 2 7 5
## 237 2 9 7
## 238 4 2 2
## 239 3 7 7
## 240 1 5 3
## 241 3 14 11
## 242 1 6 4
## 243 4 7 6
## 244 3 4 3
## 245 3 17 14
## 246 2 7 6
## 247 2 1 0
## 248 3 9 8
## 249 3 6 0
## 250 3 8 2
## 251 3 3 2
## 252 2 5 4
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## 254 3 20 8
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## 294 4 6 5
## 295 3 8 7
## 296 3 2 2
## 297 3 11 10
## 298 3 5 4
## 299 2 5 3
## 300 2 8 7
## YearsSinceLastPromotion YearsWithCurrManager
## 1 7 7
## 2 0 2
## 3 3 8
## 4 0 2
## 5 0 7
## 6 0 0
## 7 0 7
## 8 2 2
## 9 0 9
## 10 0 3
## 11 4 3
## 12 0 3
## 13 1 2
## 14 1 1
## 15 0 0
## 16 0 2
## 17 1 6
## 18 0 0
## 19 1 7
## 20 1 4
## 21 8 8
## 22 1 3
## 23 4 7
## 24 0 4
## 25 0 7
## 26 4 7
## 27 0 0
## 28 2 10
## 29 9 6
## 30 2 2
## 31 2 2
## 32 0 0
## 33 7 7
## 34 0 0
## 35 1 1
## 36 0 2
## 37 9 5
## 38 0 4
## 39 2 2
## 40 0 0
## 41 0 7
## 42 1 2
## 43 0 2
## 44 5 8
## 45 2 1
## 46 1 2
## 47 1 2
## 48 1 2
## 49 0 0
## 50 0 0
## 51 1 2
## 52 0 1
## 53 0 0
## 54 0 3
## 55 4 7
## 56 1 7
## 57 0 8
## 58 1 11
## 59 0 0
## 60 3 6
## 61 0 7
## 62 7 7
## 63 0 7
## 64 0 0
## 65 0 0
## 66 0 0
## 67 0 3
## 68 0 3
## 69 1 7
## 70 0 0
## 71 1 0
## 72 7 6
## 73 7 9
## 74 2 2
## 75 0 0
## 76 1 3
## 77 1 0
## 78 0 2
## 79 1 0
## 80 1 3
## 81 3 3
## 82 7 5
## 83 0 1
## 84 0 2
## 85 0 3
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## 87 0 2
## 88 7 7
## 89 0 2
## 90 13 8
## 91 1 5
## 92 4 7
## 93 1 2
## 94 3 6
## 95 2 1
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## 97 0 4
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## 103 0 2
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## 110 0 0
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## 234 0 4
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## 244 1 3
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## 246 7 3
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## 251 2 0
## 252 2 1
## 253 8 7
## 254 11 8
## 255 0 0
## 256 0 0
## 257 1 2
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## 260 1 1
## 261 2 2
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## 263 2 2
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## 266 1 3
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## 281 0 3
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## 285 5 11
## 286 1 0
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## 290 0 1
## 291 0 2
## 292 0 0
## 293 3 10
## 294 1 2
## 295 1 7
## 296 2 2
## 297 11 1
## 298 4 4
## 299 0 3
## 300 1 7#Packages Used
library(tidyverse)## -- Attaching packages ---------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
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library(ggplot2)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
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## nasalibrary(ggthemes)
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library(mlbench)
library(maps)##
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library(skimr)##
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## filterstr(work)## 'data.frame': 870 obs. of 36 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : int 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 859 1128 1412 2016 1646 733 1448 1105 1055 1597 ...
## $ EnvironmentSatisfaction : int 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : int 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : int 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
## $ JobSatisfaction : int 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : int 3 3 3 3 3 4 3 3 3 3 ...
## $ RelationshipSatisfaction: int 3 1 3 3 3 3 1 3 4 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 1 0 0 2 0 2 0 3 1 1 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : int 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...skim(work)## Skim summary statistics
## n obs: 870
## n variables: 36
##
## -- Variable type:factor ---------------------------------------------------------------------------------------------------------------------------------
## variable missing complete n n_unique
## Attrition 0 870 870 2
## BusinessTravel 0 870 870 3
## Department 0 870 870 3
## EducationField 0 870 870 6
## Gender 0 870 870 2
## JobRole 0 870 870 9
## MaritalStatus 0 870 870 3
## Over18 0 870 870 1
## OverTime 0 870 870 2
## top_counts ordered
## No: 730, Yes: 140, NA: 0 FALSE
## Tra: 618, Tra: 158, Non: 94, NA: 0 FALSE
## Res: 562, Sal: 273, Hum: 35, NA: 0 FALSE
## Lif: 358, Med: 270, Mar: 100, Tec: 75 FALSE
## Mal: 516, Fem: 354, NA: 0 FALSE
## Sal: 200, Res: 172, Lab: 153, Man: 87 FALSE
## Mar: 410, Sin: 269, Div: 191, NA: 0 FALSE
## Y: 870, NA: 0 FALSE
## No: 618, Yes: 252, NA: 0 FALSE
##
## -- Variable type:integer --------------------------------------------------------------------------------------------------------------------------------
## variable missing complete n mean sd p0
## Age 0 870 870 36.83 8.93 18
## DailyRate 0 870 870 815.23 401.12 103
## DistanceFromHome 0 870 870 9.34 8.14 1
## Education 0 870 870 2.9 1.02 1
## EmployeeCount 0 870 870 1 0 1
## EmployeeNumber 0 870 870 1029.83 604.79 1
## EnvironmentSatisfaction 0 870 870 2.7 1.1 1
## HourlyRate 0 870 870 65.61 20.13 30
## ID 0 870 870 435.5 251.29 1
## JobInvolvement 0 870 870 2.72 0.7 1
## JobLevel 0 870 870 2.04 1.09 1
## JobSatisfaction 0 870 870 2.71 1.11 1
## MonthlyIncome 0 870 870 6390.26 4597.7 1081
## MonthlyRate 0 870 870 14325.62 7108.38 2094
## NumCompaniesWorked 0 870 870 2.73 2.52 0
## PercentSalaryHike 0 870 870 15.2 3.68 11
## PerformanceRating 0 870 870 3.15 0.36 3
## RelationshipSatisfaction 0 870 870 2.71 1.1 1
## StandardHours 0 870 870 80 0 80
## StockOptionLevel 0 870 870 0.78 0.86 0
## TotalWorkingYears 0 870 870 11.05 7.51 0
## TrainingTimesLastYear 0 870 870 2.83 1.27 0
## WorkLifeBalance 0 870 870 2.78 0.71 1
## YearsAtCompany 0 870 870 6.96 6.02 0
## YearsInCurrentRole 0 870 870 4.2 3.64 0
## YearsSinceLastPromotion 0 870 870 2.17 3.19 0
## YearsWithCurrManager 0 870 870 4.14 3.57 0
## p25 p50 p75 p100 hist
## 30 35 43 60 <U+2581><U+2583><U+2586><U+2587><U+2585><U+2583><U+2582><U+2582>
## 472.5 817.5 1165.75 1499 <U+2586><U+2586><U+2587><U+2587><U+2587><U+2586><U+2587><U+2587>
## 2 7 14 29 <U+2587><U+2585><U+2583><U+2581><U+2581><U+2581><U+2581><U+2581>
## 2 3 4 5 <U+2582><U+2585><U+2581><U+2587><U+2581><U+2586><U+2581><U+2581>
## 1 1 1 1 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
## 477.25 1039 1561.5 2064 <U+2587><U+2587><U+2586><U+2587><U+2587><U+2587><U+2587><U+2587>
## 2 3 4 4 <U+2585><U+2581><U+2586><U+2581><U+2581><U+2587><U+2581><U+2587>
## 48 66 83 100 <U+2586><U+2587><U+2587><U+2587><U+2586><U+2587><U+2587><U+2587>
## 218.25 435.5 652.75 870 <U+2587><U+2587><U+2587><U+2587><U+2587><U+2587><U+2587><U+2587>
## 2 3 3 4 <U+2581><U+2581><U+2583><U+2581><U+2581><U+2587><U+2581><U+2581>
## 1 2 3 5 <U+2587><U+2587><U+2581><U+2583><U+2581><U+2582><U+2581><U+2581>
## 2 3 4 4 <U+2585><U+2581><U+2585><U+2581><U+2581><U+2587><U+2581><U+2587>
## 2839.5 4945.5 8182 19999 <U+2587><U+2587><U+2583><U+2582><U+2581><U+2581><U+2581><U+2581>
## 8092 14074.5 20456.25 26997 <U+2586><U+2587><U+2587><U+2587><U+2587><U+2586><U+2587><U+2586>
## 1 2 4 9 <U+2587><U+2582><U+2582><U+2582><U+2581><U+2581><U+2581><U+2581>
## 12 14 18 25 <U+2587><U+2587><U+2583><U+2583><U+2581><U+2582><U+2582><U+2581>
## 3 3 3 4 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
## 2 3 4 4 <U+2585><U+2581><U+2585><U+2581><U+2581><U+2587><U+2581><U+2587>
## 80 80 80 80 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
## 0 1 1 3 <U+2587><U+2581><U+2587><U+2581><U+2581><U+2582><U+2581><U+2581>
## 6 10 15 40 <U+2585><U+2587><U+2582><U+2582><U+2581><U+2581><U+2581><U+2581>
## 2 3 3 6 <U+2581><U+2581><U+2587><U+2587><U+2581><U+2582><U+2582><U+2581>
## 2 3 3 4 <U+2581><U+2581><U+2583><U+2581><U+2581><U+2587><U+2581><U+2582>
## 3 5 10 40 <U+2587><U+2585><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
## 2 3 7 18 <U+2587><U+2582><U+2581><U+2585><U+2581><U+2581><U+2581><U+2581>
## 0 1 3 15 <U+2587><U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
## 2 3 7 17 <U+2587><U+2582><U+2581><U+2583><U+2581><U+2581><U+2581><U+2581>count(work$Attrition)## x freq
## 1 No 730
## 2 Yes 140#Check for missing values
colSums(is.na(work))## ID Age Attrition
## 0 0 0
## BusinessTravel DailyRate Department
## 0 0 0
## DistanceFromHome Education EducationField
## 0 0 0
## EmployeeCount EmployeeNumber EnvironmentSatisfaction
## 0 0 0
## Gender HourlyRate JobInvolvement
## 0 0 0
## JobLevel JobRole JobSatisfaction
## 0 0 0
## MaritalStatus MonthlyIncome MonthlyRate
## 0 0 0
## NumCompaniesWorked Over18 OverTime
## 0 0 0
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 0 0 0
## StandardHours StockOptionLevel TotalWorkingYears
## 0 0 0
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 0 0 0
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 0 0 0#Here is a quick way we can visualize the data that was provided. This will give us the starting point to begin our analysis. #What we are looking for are variables where attirition stands out from the non-attrition values.
#Automated EDA
# step 1, save target variable name
target <- "Attrition"
# step 2, save explanator variable names
numvars <- work %>% keep(is.numeric) %>% colnames
numplot <- function(df, explan, resp) {
ggplot(data = df) + geom_density(aes_string(x = explan, fill = resp), alpha = 0.5)
}
numplot(work, explan = "MonthlyIncome", resp = "Attrition")
numplot(work, explan = "MonthlyIncome", resp = "Attrition")
plotlist <- lapply(numvars, function(x) numplot(work, x, "Attrition"))
png()
lapply(numvars, function(x) numplot(work, x, "Attrition"))## [[1]]##
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## [[27]]dev.off()## png
## 2library(cowplot)##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggthemes':
##
## theme_mapplot_grid(plotlist = plotlist)
# categorical vs categorical
ggplot(data = work) + geom_bar(aes(x = MonthlyIncome, fill = Attrition), position = "fill", alpha = 0.9) + coord_flip()
ones <- rep(1, nrow(work))
zeroes <- rep(0, nrow(work))
onezeroes <- c(ones, zeroes)
work$rcat <- sample(onezeroes, nrow(work))
ggplot(data = work) + geom_bar(aes(x = MonthlyIncome, fill = Attrition), position = "fill", alpha = 0.9) + coord_flip()
# step 1: Name target variable:
target <- "Attrition"
# step 2: name explanatory vars
expls <- work %>% keep(is.factor) %>% colnames
catplot <- function(df, x,y){
ggplot(data = df, aes_string(x = x, fill = y)) +
geom_bar(position = "fill", alpha = 0.9) +
coord_flip()
}
plotlist2 <- lapply(expls, function(x) catplot(work, x, target))
plot_grid(plotlist = plotlist2)
#Additional EDA - Scatter Plots #Here we are trying to see if we can gain any further insights into how these variables interact with attrition.
#Plots to see what is correlated with Attrition)
ggplot(work, aes(x=MonthlyRate, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=MonthlyIncome, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=JobSatisfaction, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=PercentSalaryHike, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=EnvironmentSatisfaction, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=JobSatisfaction, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=RelationshipSatisfaction, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=TotalWorkingYears, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=WorkLifeBalance, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=YearsInCurrentRole, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=YearsSinceLastPromotion, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=JobLevel, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=DistanceFromHome, y=Attrition,color=JobRole))+geom_point(position="jitter")
ggplot(work, aes(x=Education, y=Attrition,color=JobRole))+geom_point(position="jitter")
#Checking if Travel Rate has an impact
ggplot(work, aes(x=MonthlyRate, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=MonthlyIncome, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=JobSatisfaction, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=PercentSalaryHike, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=EnvironmentSatisfaction, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=JobSatisfaction, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=RelationshipSatisfaction, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=TotalWorkingYears, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=WorkLifeBalance, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=YearsInCurrentRole, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=YearsSinceLastPromotion, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=JobLevel, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=DistanceFromHome, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
ggplot(work, aes(x=Education, y=Attrition,color=BusinessTravel))+geom_point(position="jitter")
#Boxplots of data
ggplot(work, aes(x=Attrition, y=MonthlyRate))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=MonthlyIncome))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=JobSatisfaction))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=PercentSalaryHike))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=EnvironmentSatisfaction))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=JobSatisfaction))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=RelationshipSatisfaction))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=TotalWorkingYears))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=WorkLifeBalance))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=YearsInCurrentRole))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=YearsSinceLastPromotion))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=JobLevel))+geom_boxplot()
ggplot(work, aes(x=Attrition, y=DistanceFromHome))+geom_boxplot()
#Here, we drop the variables that have only one value. Variables with no standard deviation are of no use to us in this analysis.
#Drop variables with no standard deviation
work <- subset(work, select = -c(EmployeeCount, StandardHours, Over18))
Case2PredictionsSeanMcWhirter_Attrition <- subset(Case2PredictionsSeanMcWhirter_Attrition, select = -c(EmployeeCount, StandardHours, Over18))#With the correlation plot below, we are inspecting correlation between numeric variables. As we can see, there are quite a few variables that are correlated with one another.
#This could pose a problem, so we will attempt to deal with them later on if they become problematic.
#corrplot
work %>% keep(is.numeric)%>% na.omit %>% cor %>% corrplot("upper", addCoef.col = "white", number.digits = 2,
number.cex = 0.5, method="square",
order="hclust", title="Variable Corr Heatmap",
tl.srt=90, tl.cex = 0.8,)
#Feature Engineering attempt. The goal here was to combine differnet variables to try and pull the attrition density away from the non-attrition.
#Quite a few hours went into this, however no beneficial outcome came of it due to my lack of knowledge in Feature Engineering.
work_test<-work
work_test<-work_test%>%mutate(IncLevYears=((JobLevel+TotalWorkingYears+YearsAtCompany)*Age))
work_test<-work_test%>%mutate(IncLevYears2=(YearsSinceLastPromotion+YearsAtCompany))
work_test<-work_test%>%mutate(IncLevYears3=(JobInvolvement+log(MonthlyIncome)+JobLevel+TotalWorkingYears+YearsAtCompany+YearsInCurrentRole))
work_test<-work_test%>%mutate(IncLevYears4=NumCompaniesWorked+Age+YearsSinceLastPromotion)
#work<-work%>%mutate(CoRolePro=YearsInCurrentRole*YearsAtCompany*YearsSinceLastPromotion)
work_test<-work_test%>%mutate(CoRolePro2=(YearsSinceLastPromotion+YearsWithCurrManager+YearsAtCompany))
#work<-work%>%mutate(test=(((MonthlyIncome*JobInvolvement)/TotalWorkingYears)
numplot <- function(df, explan, resp) {
ggplot(data = df) + geom_density(aes_string(x = explan, fill = resp), alpha = 0.5)
}
numplot(work_test, explan = "IncLevYears", resp = "Attrition")
numplot(work_test, explan = "IncLevYears2", resp = "Attrition")
numplot(work_test, explan = "IncLevYears3", resp = "Attrition")
numplot(work_test, explan = "IncLevYears4", resp = "Attrition")
numplot(work_test, explan = "CoRolePro2", resp = "Attrition")
#numplot(work, explan = "test", resp = "Attrition")
work_test%>%ggplot(aes(x=IncLevYears3, y=CoRolePro2, color=Attrition))+geom_point(position="jitter")
#Here we are converting the factored variables into integer to be used with Knn
#Converting for the training set
work$JobRole <- as.integer(work$JobRole)
work$OverTime <- as.integer(work$OverTime)
work$BusinessTravel <- as.integer(work$BusinessTravel)
work$Department <- as.integer(work$Department)
work$EducationField <- as.integer(work$EducationField)
work$MaritalStatus <- as.integer(work$MaritalStatus)
work$OverTime <- as.integer(work$OverTime)
work$Gender <- as.integer(work$Gender)
#Converting for the test set
Case2PredictionsSeanMcWhirter_Attrition$JobRole <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$JobRole)
Case2PredictionsSeanMcWhirter_Attrition$OverTime <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$OverTime)
Case2PredictionsSeanMcWhirter_Attrition$BusinessTravel <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$BusinessTravel)
Case2PredictionsSeanMcWhirter_Attrition$Department <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$Department)
Case2PredictionsSeanMcWhirter_Attrition$EducationField <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$EducationField)
Case2PredictionsSeanMcWhirter_Attrition$MaritalStatus <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$MaritalStatus)
Case2PredictionsSeanMcWhirter_Attrition$OverTime <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$OverTime)
Case2PredictionsSeanMcWhirter_Attrition$Gender <- as.integer(Case2PredictionsSeanMcWhirter_Attrition$Gender)#Standardize data for KNN
##Standardize all int columns
work<-work%>%mutate_if(is.numeric,scale)
#For the blank set, we need to keep the ID value as-is so we can use it with our final prediction output.
Case2PredictionsSeanMcWhirter_Attrition<-Case2PredictionsSeanMcWhirter_Attrition%>%mutate_at(vars(-ID),scale)
#Confirm it worked and get final column numbers
#str(work)
#str(Case2PredictionsSeanMcWhirter_Attrition)
#skim(work_norm2)#KNN Variable Selection–This will run all of the variables and produce the most important ones.
#install.packages("ISLR")
#library(ISLR)
#library(caret)
set.seed(300)
#Spliting data as training and test set. Using createDataPartition() function from caret
indxTrain <- createDataPartition(y = work$Attrition,p = 0.75,list = FALSE)
training <- work[indxTrain,]
testing <- work[-indxTrain,]
#Checking distibution in origanl data and partitioned data
prop.table(table(training$Attrition)) * 100##
## No Yes
## 83.92037 16.07963prop.table(table(testing$Attrition)) * 100##
## No Yes
## 83.87097 16.12903prop.table(table(work$Attrition)) * 100##
## No Yes
## 83.90805 16.09195#Training the model
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(3333)
knn_fit <- caret::train(Attrition ~., data = training, method = "knn",
trControl=trctrl,
preProcess = c("center", "scale"),
tuneLength = 10)
knn_fit## k-Nearest Neighbors
##
## 653 samples
## 33 predictor
## 2 classes: 'No', 'Yes'
##
## Pre-processing: centered (33), scaled (33)
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 587, 588, 587, 588, 587, 587, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.8468850 0.15968703
## 7 0.8464270 0.11363743
## 9 0.8468930 0.09227393
## 11 0.8443364 0.06387508
## 13 0.8453620 0.05865069
## 15 0.8423084 0.03285418
## 17 0.8433263 0.04289187
## 19 0.8438469 0.04777738
## 21 0.8423162 0.03795144
## 23 0.8418111 0.03318954
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 9.knnPredict<-predict(knn_fit, newdata=testing)
confusionMatrix(knnPredict, testing$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 181 34
## Yes 1 1
##
## Accuracy : 0.8387
## 95% CI : (0.7829, 0.885)
## No Information Rate : 0.8387
## P-Value [Acc > NIR] : 0.545
##
## Kappa : 0.0373
##
## Mcnemar's Test P-Value : 6.338e-08
##
## Sensitivity : 0.99451
## Specificity : 0.02857
## Pos Pred Value : 0.84186
## Neg Pred Value : 0.50000
## Prevalence : 0.83871
## Detection Rate : 0.83410
## Detection Prevalence : 0.99078
## Balanced Accuracy : 0.51154
##
## 'Positive' Class : No
## #knn_fit 2 --> for ROC maximization
ctrl <- trainControl(method = "repeatedcv", repeats = 5, classProbs=TRUE, summaryFunction = twoClassSummary)
kNNFit4 <- caret::train(Attrition ~ .,
data = work,
method = "knn",
tuneLength = 15,
trControl = ctrl,
preProc = c("center", "scale"))## Warning in train.default(x, y, weights = w, ...): The metric "Accuracy" was
## not in the result set. ROC will be used instead.kNNFit4## k-Nearest Neighbors
##
## 870 samples
## 33 predictor
## 2 classes: 'No', 'Yes'
##
## Pre-processing: centered (33), scaled (33)
## Resampling: Cross-Validated (10 fold, repeated 5 times)
## Summary of sample sizes: 783, 783, 783, 783, 783, 783, ...
## Resampling results across tuning parameters:
##
## k ROC Sens Spec
## 5 0.6865068 0.9887671 0.162857143
## 7 0.6918102 0.9931507 0.104285714
## 9 0.6977397 0.9945205 0.074285714
## 11 0.7050196 0.9956164 0.062857143
## 13 0.7088552 0.9978082 0.047142857
## 15 0.7142172 0.9986301 0.041428571
## 17 0.7210372 1.0000000 0.034285714
## 19 0.7295303 1.0000000 0.032857143
## 21 0.7300783 1.0000000 0.027142857
## 23 0.7339530 1.0000000 0.027142857
## 25 0.7415851 1.0000000 0.028571429
## 27 0.7504403 1.0000000 0.025714286
## 29 0.7513307 1.0000000 0.022857143
## 31 0.7520352 1.0000000 0.015714286
## 33 0.7550294 1.0000000 0.008571429
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was k = 33.#####Ranking of variable importance
library(mlbench)
control <- caret::trainControl(method="repeatedcv", number=10, repeats=3)
#Train Model
model<- caret::train(Attrition ~ . , data=work, method="lvq", preProcess="scale" , trControl=control)
#Importance estimate
importance<-varImp(model, scale=FALSE)
print(importance)## ROC curve variable importance
##
## only 20 most important variables shown (out of 33)
##
## Importance
## OverTime 0.6679
## MonthlyIncome 0.6567
## TotalWorkingYears 0.6563
## YearsAtCompany 0.6470
## StockOptionLevel 0.6455
## MaritalStatus 0.6438
## JobLevel 0.6406
## YearsInCurrentRole 0.6403
## YearsWithCurrManager 0.6291
## Age 0.6265
## JobInvolvement 0.6159
## JobSatisfaction 0.5833
## JobRole 0.5829
## Department 0.5605
## DistanceFromHome 0.5586
## EnvironmentSatisfaction 0.5532
## WorkLifeBalance 0.5491
## TrainingTimesLastYear 0.5428
## Education 0.5384
## ID 0.5371plot(importance)
#Here we will rank the variables by importance. This is just another model we can use to confirm the variable importance using Recursive Feature Elimination
####Ranking of variable importance via Recursive Feature Elimination (RFE)
set.seed(7)
# define the control using a random forest selection function
control <- rfeControl(functions=rfFuncs, method="cv", number=3)
# run the RFE algorithm
results <- rfe(work[,3:32], work[,2], sizes=c(3:32), rfeControl=control)## Warning in rfout$mse/(var(y) * (n - 1)/n): Recycling array of length 1 in vector-array arithmetic is deprecated.
## Use c() or as.vector() instead.# summarize the results
#print(results)
# list the chosen features
predictors(results)## [1] "TotalWorkingYears" "NumCompaniesWorked"
## [3] "YearsAtCompany" "MonthlyIncome"
## [5] "JobLevel" "Education"
## [7] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [9] "Attrition" "MaritalStatus"
## [11] "MonthlyRate" "HourlyRate"
## [13] "JobRole" "WorkLifeBalance"
## [15] "StockOptionLevel" "BusinessTravel"
## [17] "EducationField" "DailyRate"
## [19] "Gender" "EmployeeNumber"
## [21] "OverTime" "PercentSalaryHike"
## [23] "JobInvolvement" "RelationshipSatisfaction"# plot the results
plot(results, type=c("g", "o"))
#str(work)KNN
#KNN
set.seed(100)
splitPerc = .70
##If we needed to ensure we are omitting NAs
#work <- work_norm2 %>% na.omit()
#test/train split
trainIndices = sample(1:dim(work)[1],round(splitPerc * dim(work)[1]))
train = work[trainIndices,]
test = work[-trainIndices,]
var1=22
var2=19
var3=26
var4=16
var5=15
var6=18
k_num=3
#KNN Test
classifications = knn(train[,c(var1, var2, var3, var4, var5, var6)],test[,c(var1, var2, var3, var4, var5, var6)],train$Attrition, prob = TRUE, k = k_num)
table(classifications,test$Attrition)##
## classifications No Yes
## No 206 21
## Yes 14 20CM1=confusionMatrix(table(classifications, test$Attrition))
#Copy of knn from function
classifications = knn(train[,c(var1, var2, var3, var4, var5, var6)],test[,c(var1, var2, var3, var4, var5, var6)],train$Attrition, prob = TRUE, k = k_num)
table(test$Attrition,classifications)## classifications
## No Yes
## No 206 14
## Yes 21 20CM2 = confusionMatrix(table(test$Attrition,classifications))
## Loop for many k and one training / test partition--determining the optimal number of k.
iterations=50
accs = data.frame(accuracy = numeric(iterations), sensitivity=numeric(iterations), specificity=numeric(iterations), k = numeric(iterations))
for(i in 1:iterations)
{
classifications = knn(train[,c(var1, var2, var3, var4, var5, var6)],test[,c(var1, var2, var3, var4, var5, var6)],train$Attrition, prob = TRUE, k = i)
table(classifications,test$Attrition)
CM = confusionMatrix(table(classifications, test$Attrition))
accs$accuracy[i] = CM$overall[1]
accs$sensitivity[i]=CM$byClass[1]
accs$specificity[i]=CM$byClass[2]
accs$k[i] = i
}
plot(accs$k,accs$accuracy, type = "l", xlab = "k", ylab="Accuracy")
plot(accs$k,accs$sensitivity, type = "l", xlab = "k", ylab="sensitivity")
plot(accs$k,accs$specificity, type = "l", xlab = "k", ylab="specificity")
accs## accuracy sensitivity specificity k
## 1 0.8160920 0.8954545 0.3902439 1
## 2 0.8199234 0.8909091 0.4390244 2
## 3 0.8659004 0.9363636 0.4878049 3
## 4 0.8582375 0.9363636 0.4390244 4
## 5 0.8773946 0.9545455 0.4634146 5
## 6 0.8659004 0.9500000 0.4146341 6
## 7 0.8697318 0.9545455 0.4146341 7
## 8 0.8582375 0.9590909 0.3170732 8
## 9 0.8697318 0.9681818 0.3414634 9
## 10 0.8659004 0.9636364 0.3414634 10
## 11 0.8697318 0.9681818 0.3414634 11
## 12 0.8735632 0.9727273 0.3414634 12
## 13 0.8582375 0.9681818 0.2682927 13
## 14 0.8697318 0.9727273 0.3170732 14
## 15 0.8659004 0.9772727 0.2682927 15
## 16 0.8620690 0.9727273 0.2682927 16
## 17 0.8659004 0.9772727 0.2682927 17
## 18 0.8659004 0.9772727 0.2682927 18
## 19 0.8620690 0.9772727 0.2439024 19
## 20 0.8659004 0.9727273 0.2926829 20
## 21 0.8544061 0.9681818 0.2439024 21
## 22 0.8620690 0.9772727 0.2439024 22
## 23 0.8582375 0.9681818 0.2682927 23
## 24 0.8544061 0.9681818 0.2439024 24
## 25 0.8467433 0.9590909 0.2439024 25
## 26 0.8467433 0.9590909 0.2439024 26
## 27 0.8467433 0.9590909 0.2439024 27
## 28 0.8467433 0.9590909 0.2439024 28
## 29 0.8467433 0.9590909 0.2439024 29
## 30 0.8544061 0.9681818 0.2439024 30
## 31 0.8544061 0.9727273 0.2195122 31
## 32 0.8505747 0.9636364 0.2439024 32
## 33 0.8544061 0.9636364 0.2682927 33
## 34 0.8505747 0.9681818 0.2195122 34
## 35 0.8429119 0.9590909 0.2195122 35
## 36 0.8467433 0.9590909 0.2439024 36
## 37 0.8505747 0.9681818 0.2195122 37
## 38 0.8505747 0.9681818 0.2195122 38
## 39 0.8429119 0.9590909 0.2195122 39
## 40 0.8429119 0.9590909 0.2195122 40
## 41 0.8429119 0.9590909 0.2195122 41
## 42 0.8390805 0.9545455 0.2195122 42
## 43 0.8429119 0.9590909 0.2195122 43
## 44 0.8429119 0.9590909 0.2195122 44
## 45 0.8429119 0.9590909 0.2195122 45
## 46 0.8390805 0.9545455 0.2195122 46
## 47 0.8429119 0.9590909 0.2195122 47
## 48 0.8429119 0.9590909 0.2195122 48
## 49 0.8429119 0.9590909 0.2195122 49
## 50 0.8429119 0.9590909 0.2195122 50CM1## Confusion Matrix and Statistics
##
##
## classifications No Yes
## No 206 21
## Yes 14 20
##
## Accuracy : 0.8659
## 95% CI : (0.8185, 0.9048)
## No Information Rate : 0.8429
## P-Value [Acc > NIR] : 0.1753
##
## Kappa : 0.4558
##
## Mcnemar's Test P-Value : 0.3105
##
## Sensitivity : 0.9364
## Specificity : 0.4878
## Pos Pred Value : 0.9075
## Neg Pred Value : 0.5882
## Prevalence : 0.8429
## Detection Rate : 0.7893
## Detection Prevalence : 0.8697
## Balanced Accuracy : 0.7121
##
## 'Positive' Class : No
## CM2## Confusion Matrix and Statistics
##
## classifications
## No Yes
## No 206 14
## Yes 21 20
##
## Accuracy : 0.8659
## 95% CI : (0.8185, 0.9048)
## No Information Rate : 0.8697
## P-Value [Acc > NIR] : 0.6166
##
## Kappa : 0.4558
##
## Mcnemar's Test P-Value : 0.3105
##
## Sensitivity : 0.9075
## Specificity : 0.5882
## Pos Pred Value : 0.9364
## Neg Pred Value : 0.4878
## Prevalence : 0.8697
## Detection Rate : 0.7893
## Detection Prevalence : 0.8429
## Balanced Accuracy : 0.7479
##
## 'Positive' Class : No
## #The ouput from the graph below shows that the optimal K=3KNN for k-iterations and k-test/training sets
##Loop for many k values and many test/training sets. This will tell us how our classifier acts over may differnet random samples of training/test sets as well as k-values.
iterations=50
numks=30
masterAcc = matrix(nrow=iterations, ncol=numks)
massterSens=matrix(nrow=iterations, ncol=numks)
masterSpec=matrix(nrow=iterations, ncol=numks)
for(j in 1:iterations)
{
accs=data.frame(accuracy=numeric(30), k=numeric(30))
trainIndicies=sample(1:dim(work)[1],round(splitPerc*dim(work)[1]))
train=work[trainIndicies,]
test=work[-trainIndicies,]
for(i in 1:numks)
{
classifications=knn(train[,c(var1, var2, var3, var4, var5, var6)], test[,c(var1, var2, var3, var4, var5, var6)], train$Attrition, prob=TRUE, k=i)
table(classifications, test$Attrition)
CM=confusionMatrix(table(classifications, test$Attrition))
masterAcc[j,i]=CM$overall[1]
massterSens[j,i]=CM$byClass[1]
masterSpec[j,i]=CM$byClass[2]
}
}
MeanAcc = colMeans(masterAcc)
MeanSens = colMeans(massterSens)
MeanSpec = colMeans(masterSpec)
plot(seq(1,numks,1),MeanAcc, type = "l")
plot(seq(1,numks,1),MeanSens, type = "l")
plot(seq(1,numks,1),MeanSpec, type = "l")
#The outcome confirms that lower k-value yields higher specificity #Use the classifier to make the predictions for the blank data set
#Make Attrition numeric for "predict" to work
#set.seed(400)
#Dropping All columns but necessary ones
#work_pred <- subset(work, select = c(ID, OverTime, MonthlyIncome, StockOptionLevel, JobRole, JobLevel, MaritalStatus))
#Output of kNN fit
#knnFit
#Case2PredictionsSeanMcWhirter_Attrition <- predict(knnFit,newdata = Case2PredictionsSeanMcWhirter_Attrition )
#Make Predictions
#Predictions=predict(classifications, newdata=Case2PredictionsSeanMcWhirter_Attrition)
#Case2PredictionsSeanMcWhirter_Attrition<-data.frame(ID=Case2PredictionsSeanMcWhirter_Attrition$ID, Predicted_Income=Predictions)
#Write predicitons to CSV for submission
#write.csv(Case2PredictionsSeanMcWhirter_Attrition, file="Case2PredictionsSeanMcWhirter_Attrition.csv", row.names = FALSE)#Use the classifier to make the predictions for the blank data set
#train_pred <- subset(work, select = c(OverTime, MonthlyIncome, StockOptionLevel, JobRole, JobLevel, MaritalStatus, Attrition))
#predictions <- subset(Case2PredictionsSeanMcWhirter_Attrition, select = c(OverTime, MonthlyIncome, StockOptionLevel, JobRole, JobLevel, MaritalStatus))
#classification = knn(train_pred[,1:6], predictions[,1:6], train_pred$Attrition, k=3, prob=TRUE)
#Case2PredictionsSeanMcWhirter_Attrition$Attrition <- data.frame(classification)
#Case2PredictionsSeanMcWhirter_Attrition$Attrition
#Case2PredictionsSeanMcWhirter_Attrition<-data.frame(ID=Case2PredictionsSeanMcWhirter_Attrition$ID, Case2PredictionsSeanMcWhirter_Attrition$Attrition)
#write.csv(Case2PredictionsSeanMcWhirter_Attrition, file="Case2PredictionsSeanMcWhirter_Attrition.csv", row.names = FALSE)Linear Regression for Monthly Income
#Reset Data
work=read.csv("C:/Users/seans/Desktop/DDS/Unit 14 and 15 Case Study 2/CaseStudy2-data.csv", header=TRUE, sep=",", stringsAsFactors = TRUE)
#Drop variables with no standard deviation as well as any income data
work <- subset(work, select = -c(EmployeeCount, StandardHours, Over18, ID, MonthlyRate, DailyRate))
library(olsrr)##
## Attaching package: 'olsrr'## The following object is masked from 'package:datasets':
##
## rivers#Model for Monthly Income
model<-lm(MonthlyIncome ~ . , data=work)
# use stepwise variable selection (ols_step_forward_p(model))
ols_step_both_p(model)## Stepwise Selection Method
## ---------------------------
##
## Candidate Terms:
##
## 1. Age
## 2. Attrition
## 3. BusinessTravel
## 4. Department
## 5. DistanceFromHome
## 6. Education
## 7. EducationField
## 8. EmployeeNumber
## 9. EnvironmentSatisfaction
## 10. Gender
## 11. HourlyRate
## 12. JobInvolvement
## 13. JobLevel
## 14. JobRole
## 15. JobSatisfaction
## 16. MaritalStatus
## 17. NumCompaniesWorked
## 18. OverTime
## 19. PercentSalaryHike
## 20. PerformanceRating
## 21. RelationshipSatisfaction
## 22. StockOptionLevel
## 23. TotalWorkingYears
## 24. TrainingTimesLastYear
## 25. WorkLifeBalance
## 26. YearsAtCompany
## 27. YearsInCurrentRole
## 28. YearsSinceLastPromotion
## 29. YearsWithCurrManager
##
## We are selecting variables based on p value...
##
## Variables Entered/Removed:
##
## - JobLevel added
## - JobRole added
## - TotalWorkingYears added
## - BusinessTravel added
## - Gender added
##
## No more variables to be added/removed.
##
##
## Final Model Output
## ------------------
##
## Model Summary
## -------------------------------------------------------------------
## R 0.974 RMSE 1054.722
## R-Squared 0.948 Coef. Var 16.505
## Adj. R-Squared 0.947 MSE 1112439.436
## Pred R-Squared 0.947 MAE 797.414
## -------------------------------------------------------------------
## RMSE: Root Mean Square Error
## MSE: Mean Square Error
## MAE: Mean Absolute Error
##
## ANOVA
## ---------------------------------------------------------------------------------
## Sum of
## Squares DF Mean Square F Sig.
## ---------------------------------------------------------------------------------
## Regression 17417376230.084 13 1339798171.545 1204.379 0.0000
## Residual 952248157.111 856 1112439.436
## Total 18369624387.195 869
## ---------------------------------------------------------------------------------
##
## Parameter Estimates
## ------------------------------------------------------------------------------------------------------------------
## model Beta Std. Error Std. Beta t Sig lower upper
## ------------------------------------------------------------------------------------------------------------------
## (Intercept) -478.919 226.410 -2.115 0.035 -923.303 -34.536
## JobLevel 2792.584 81.269 0.662 34.362 0.000 2633.073 2952.094
## JobRoleHuman Resources -374.979 250.206 -0.014 -1.499 0.134 -866.069 116.110
## JobRoleLaboratory Technician -625.023 168.108 -0.052 -3.718 0.000 -954.974 -295.071
## JobRoleManager 3985.659 227.584 0.204 17.513 0.000 3538.971 4432.347
## JobRoleManufacturing Director 118.224 166.250 0.008 0.711 0.477 -208.081 444.529
## JobRoleResearch Director 3974.664 213.077 0.203 18.654 0.000 3556.450 4392.879
## JobRoleResearch Scientist -345.383 168.272 -0.030 -2.053 0.040 -675.657 -15.109
## JobRoleSales Executive -74.387 143.166 -0.007 -0.520 0.603 -355.385 206.612
## JobRoleSales Representative -405.032 211.162 -0.021 -1.918 0.055 -819.488 9.424
## TotalWorkingYears 48.192 7.902 0.079 6.099 0.000 32.682 63.701
## BusinessTravelTravel_Frequently 214.458 138.887 0.018 1.544 0.123 -58.141 487.057
## BusinessTravelTravel_Rarely 391.128 117.343 0.039 3.333 0.001 160.815 621.441
## GenderMale 132.512 73.395 0.014 1.805 0.071 -11.544 276.567
## ------------------------------------------------------------------------------------------------------------------##
## Stepwise Selection Summary
## ----------------------------------------------------------------------------------------------------
## Added/ Adj.
## Step Variable Removed R-Square R-Square C(p) AIC RMSE
## ----------------------------------------------------------------------------------------------------
## 1 JobLevel addition 0.906 0.906 679.7640 15094.3534 1413.2958
## 2 JobRole addition 0.945 0.944 37.7030 14641.4330 1084.4355
## 3 TotalWorkingYears addition 0.947 0.947 3.0050 14607.2848 1062.7570
## 4 BusinessTravel addition 0.948 0.947 -7.7670 14598.3432 1056.1120
## 5 Gender addition 0.948 0.947 -9.0000 14597.0365 1054.7224
## ----------------------------------------------------------------------------------------------------#Regression Cross Validation - Stepwise
#Using stepwise selection
#Here we are chanign th factored variables to numeric so cross-validation will work
work$Attrition <- as.integer(work$Attrition)
work$JobRole <- as.integer(work$JobRole)
work$OverTime <- as.integer(work$OverTime)
work$BusinessTravel <- as.integer(work$BusinessTravel)
work$Department <- as.integer(work$Department)
work$EducationField <- as.integer(work$EducationField)
work$MaritalStatus <- as.integer(work$MaritalStatus)
work$OverTime <- as.integer(work$OverTime)
work$Gender <- as.integer(work$Gender)
trainIndicies=sample(1:dim(work)[1],round(.75*dim(work)[1]))
train=work[trainIndicies,]
test=work[-trainIndicies,]
model1_fit=lm(MonthlyIncome~JobLevel+TotalWorkingYears+BusinessTravel, data=train)
summary(model1_fit)##
## Call:
## lm(formula = MonthlyIncome ~ JobLevel + TotalWorkingYears + BusinessTravel,
## data = train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5414.1 -868.4 61.1 719.8 3900.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2280.45 238.23 -9.572 < 2e-16 ***
## JobLevel 3655.47 79.50 45.983 < 2e-16 ***
## TotalWorkingYears 67.71 11.32 5.982 3.65e-09 ***
## BusinessTravel 183.95 80.57 2.283 0.0227 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1382 on 648 degrees of freedom
## Multiple R-squared: 0.9079, Adjusted R-squared: 0.9074
## F-statistic: 2128 on 3 and 648 DF, p-value: < 2.2e-16model1_preds=predict(model1_fit, newdata=test)
as.data.frame(model1_preds)## model1_preds
## 2 17970.589
## 6 9663.219
## 8 6123.991
## 11 17970.589
## 12 1762.089
## 16 5823.800
## 18 5688.386
## 21 6462.524
## 25 5988.578
## 31 6007.749
## 33 14789.064
## 35 6123.991
## 36 2081.451
## 38 6665.644
## 46 2555.398
## 48 9847.168
## 52 18715.362
## 53 2603.933
## 62 9914.875
## 65 1994.574
## 66 2400.814
## 67 6733.350
## 69 2081.451
## 71 2739.347
## 72 5988.578
## 79 10427.163
## 85 6801.057
## 89 9663.219
## 94 6259.404
## 96 6936.470
## 100 2400.814
## 101 2333.107
## 107 5891.506
## 112 14653.651
## 113 10456.528
## 120 6530.231
## 127 2419.984
## 129 6936.470
## 130 14585.945
## 136 5940.042
## 141 15127.597
## 147 2468.520
## 158 14131.169
## 160 10407.992
## 170 2671.640
## 171 6259.404
## 177 18173.709
## 179 9663.219
## 180 6394.817
## 181 10253.408
## 185 2284.571
## 194 6530.231
## 197 2603.933
## 203 6530.231
## 208 6733.350
## 211 14315.118
## 215 2265.400
## 219 2265.400
## 223 5688.386
## 228 2197.694
## 240 10524.234
## 248 2129.987
## 254 2062.281
## 255 9953.217
## 258 6123.991
## 261 1946.038
## 263 2468.520
## 265 2333.107
## 266 2333.107
## 267 13792.636
## 269 6007.749
## 274 6327.111
## 276 18444.535
## 279 5988.578
## 283 6394.817
## 285 2400.814
## 288 2129.987
## 289 9730.926
## 290 2129.987
## 296 2265.400
## 301 6075.455
## 302 9663.219
## 304 6259.404
## 309 6549.401
## 312 14382.825
## 317 2149.158
## 319 6123.991
## 321 1897.502
## 323 2419.984
## 325 14315.118
## 326 1994.574
## 328 6191.698
## 329 6752.521
## 335 18212.050
## 341 2333.107
## 344 1810.625
## 346 14315.118
## 354 5756.093
## 355 10795.061
## 358 2333.107
## 362 1810.625
## 366 15146.768
## 368 6191.698
## 373 10678.818
## 378 6530.231
## 381 17970.589
## 393 1829.796
## 399 6094.626
## 401 6327.111
## 405 2333.107
## 407 6462.524
## 409 14992.184
## 411 2333.107
## 420 9914.875
## 423 6394.817
## 426 10456.528
## 431 5920.871
## 443 2265.400
## 450 5891.506
## 451 6259.404
## 459 2265.400
## 461 2284.571
## 462 2942.466
## 476 6346.282
## 481 5988.578
## 488 2265.400
## 496 14856.771
## 498 2013.745
## 499 6733.350
## 500 5891.506
## 519 6259.404
## 526 11085.058
## 534 5823.800
## 539 2333.107
## 540 6191.698
## 542 2874.760
## 552 2333.107
## 558 2333.107
## 570 14382.825
## 573 5872.335
## 574 6259.404
## 575 5988.578
## 576 6056.284
## 589 2468.520
## 592 6665.644
## 593 2081.451
## 594 2603.933
## 598 6936.470
## 599 5988.578
## 601 1626.676
## 602 5823.800
## 617 2468.520
## 618 6191.698
## 620 1626.676
## 622 6752.521
## 625 2284.571
## 629 10611.112
## 630 9847.168
## 632 10185.701
## 633 6868.764
## 636 1878.332
## 640 2265.400
## 642 6597.937
## 658 14469.702
## 664 2129.987
## 672 5988.578
## 673 5736.922
## 676 18309.122
## 693 6530.231
## 697 14266.583
## 700 18241.415
## 701 1994.574
## 705 2400.814
## 709 2265.400
## 711 9847.168
## 715 9546.977
## 716 1994.574
## 718 6259.404
## 721 9914.875
## 727 2419.984
## 728 2081.451
## 733 1810.625
## 743 6259.404
## 747 2197.694
## 748 6346.282
## 749 2333.107
## 769 6394.817
## 771 6007.749
## 779 2032.916
## 780 2197.694
## 785 5485.267
## 786 13502.639
## 790 2062.281
## 794 10388.821
## 795 5688.386
## 797 14315.118
## 803 14315.118
## 804 9847.168
## 806 10253.408
## 810 2333.107
## 811 6026.919
## 814 18376.829
## 824 1878.332
## 827 9914.875
## 831 5823.800
## 833 2333.107
## 838 6259.404
## 840 18512.242
## 841 1926.867
## 844 9914.875
## 847 1994.574
## 850 2284.571
## 852 2603.933
## 853 6259.404
## 856 1994.574
## 858 9730.926
## 859 2400.814
## 866 9982.582#Find MSPE
MSPE=data.frame(Observed=test$MonthlyIncome, Predicted=model1_preds)
MSPE$Residual=MSPE$Observed-MSPE$Predicted
MSPE$SquaredResidual=MSPE$Residual^2
#MSPE
mean(MSPE$SquaredResidual)## [1] 1930736#The cross-validation yeielded a very acceptable Root MSPE of 1,311--This fits our requirements#Use the model to predict for the data set with no income figures
#Predict for No Salary CSV
#Reset Datasets so they are same length
#work=read.csv("C:/Users/seans/Desktop/DDS/Unit 14 and 15 Case Study 2/CaseStudy2-data.csv", header=TRUE, sep=",", stringsAsFactors = TRUE)
#Upload No Salary CSV
#Case2PredictionsSeanMcWhirter=read.csv("C:/Users/seans/Desktop/DDS/Unit 14 and 15 Case Study 2/CaseStudy2CompSetNoSalary.csv", header=TRUE, sep=",", stringsAsFactors = TRUE)
#Case2PredictionsSeanMcWhirter$Attrition <- as.integer(Case2PredictionsSeanMcWhirter$Attrition)
#Case2PredictionsSeanMcWhirter$JobRole <- as.integer(Case2PredictionsSeanMcWhirter$JobRole)
#Case2PredictionsSeanMcWhirter$OverTime <- as.integer(Case2PredictionsSeanMcWhirter$OverTime)
#Case2PredictionsSeanMcWhirter$BusinessTravel <- as.integer(Case2PredictionsSeanMcWhirter$BusinessTravel)
#Case2PredictionsSeanMcWhirter$Department <- as.integer(Case2PredictionsSeanMcWhirter$Department)
#Case2PredictionsSeanMcWhirter$EducationField <- as.integer(Case2PredictionsSeanMcWhirter$EducationField)
#Case2PredictionsSeanMcWhirter$MaritalStatus <- as.integer(Case2PredictionsSeanMcWhirter$MaritalStatus)
#Case2PredictionsSeanMcWhirter$OverTime <- as.integer(Case2PredictionsSeanMcWhirter$OverTime)
#Case2PredictionsSeanMcWhirter$Gender <- as.integer(Case2PredictionsSeanMcWhirter$Gender)
#Use model to make predictions
#model1_fit=lm(MonthlyIncome~JobLevel+TotalWorkingYears+BusinessTravel, data=train)
#Predictions=predict(model1_fit, newdata=Case2PredictionsSeanMcWhirter)
#Case2PredictionsSeanMcWhirter<-data.frame(ID=Case2PredictionsSeanMcWhirter$ID, Predicted_Income=Predictions)
#Write predicitons to CSV for submission
#write.csv(Case2PredictionsSeanMcWhirter, file="Case2Predictions_SeanMcWhirter.csv", row.names = FALSE)